Merge pull request #280 from MervinPraison/develop

AI Agents with memory

.github/workflows/release.yml

@@ -0,0 +1,524 @@
+name: Release Workflow
+
+on:
+  workflow_dispatch:
+    inputs:
+      tag:
+        description: 'Tag to create release with (e.g., v2.0.10)'
+        required: true
+      fork_owner:
+        description: 'GitHub username of the fork owner'
+        required: true
+        default: 'tosin2013'
+
+jobs:
+  test-ubuntu:
+    strategy:
+      matrix:
+        os: [ubuntu-22.04, ubuntu-24.04]
+        framework: ['crewai', 'autogen']
+      fail-fast: false
+
+    runs-on: ${{ matrix.os }}
+    timeout-minutes: 90
+
+    steps:
+    # First checkout the upstream repo at the specified tag
+    - name: Checkout upstream repo
+      uses: actions/checkout@v4
+      with:
+        repository: MervinPraison/PraisonAI
+        ref: ${{ github.event.inputs.tag }}
+        fetch-depth: 0
+
+    # Get the commit hash from the tag
+    - name: Get commit hash
+      id: get_commit
+      run: |
+        COMMIT_HASH=$(git rev-parse HEAD)
+        echo "commit_hash=$COMMIT_HASH" >> $GITHUB_OUTPUT
+        echo "Found commit: $COMMIT_HASH for tag ${{ github.event.inputs.tag }}"
+
+    # Now checkout the fork at main branch
+    - name: Checkout fork
+      uses: actions/checkout@v4
+      with:
+        repository: ${{ github.event.inputs.fork_owner }}/PraisonAI
+        ref: main
+        fetch-depth: 0
+        clean: true
+
+    # Sync with upstream commit
+    - name: Sync with upstream
+      run: |
+        git config --global user.name "GitHub Actions"
+        git config --global user.email "[email protected]"
+        
+        # Add upstream remote
+        git remote add upstream https://github.com/MervinPraison/PraisonAI.git
+        
+        # Fetch from upstream
+        git fetch upstream --tags --force
+        
+        # Reset to upstream commit
+        git reset --hard ${{ steps.get_commit.outputs.commit_hash }}
+
+    - name: Setup Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.11'
+        cache: 'pip'
+
+    - name: Install Poetry
+      run: |
+        curl -sSL https://install.python-poetry.org | python3 -
+        echo "$HOME/.local/bin" >> $GITHUB_PATH
+        poetry config virtualenvs.in-project true
+        poetry config virtualenvs.create true
+        poetry --version
+        poetry config --list
+
+    - name: Install Dependencies
+      run: |
+        poetry lock #--no-update
+        poetry install --all-extras
+        poetry env info
+        # Verify venv exists
+        test -d .venv || { echo "Virtual environment not created!"; exit 1; }
+        test -f .venv/bin/activate || { echo "Activation script not found!"; exit 1; }
+
+    - name: Run setup script
+      timeout-minutes: 20
+      run: |
+        set -e  # Exit on any error
+        curl -OL https://raw.githubusercontent.com/tosin2013/PraisonAI/refs/heads/main/setup.sh
+        chmod +x setup.sh
+        ./setup.sh --all --cicd || { echo "Setup script failed"; exit 1; }
+      env:
+        OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Test Basic Functionality
+      run: |
+        set -e  # Exit on any error
+        
+        # Random sleep between 60-120 seconds to avoid API rate limits
+        SLEEP_TIME=$((RANDOM % 61 + 60))
+        echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
+        sleep $SLEEP_TIME
+        
+        lsof -ti:8084 | xargs -r kill -9 || true
+        source .venv/bin/activate || { echo "Failed to activate virtual environment"; exit 1; }
+        mkdir -p test_output || { echo "Failed to create test_output directory"; exit 1; }
+        
+        echo "Testing basic CLI functionality..."
+        
+        # Test auto generation with explicit framework
+        praisonai --framework ${{ matrix.framework }}  --auto "create a short test script" 2>&1 | tee test_output/auto_test.log
+        
+        # Verify the output file exists and has content
+        if [[ ! -s test_output/auto_test.log ]]; then
+          echo "Error: No output generated from auto test"
+          exit 1
+        fi
+        
+        # Test auto generation without framework
+        echo "Basic functionality tests passed!"
+      env:
+        OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        LOGLEVEL: "debug"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Test Framework-specific Features
+      continue-on-error: false
+      run: |
+        source .venv/bin/activate
+        
+        # Random sleep between 60-120 seconds to avoid API rate limits
+        SLEEP_TIME=$((RANDOM % 61 + 60))
+        echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
+        sleep $SLEEP_TIME
+        
+        lsof -ti:8084 | xargs -r kill -9 || true
+        praisonai --framework ${{ matrix.framework }} --auto "create a movie script about Robots in Mars" 2>&1 | tee test_output/framework_output.txt || echo "Framework-specific test failed" >> test_output/errors.log
+      env:
+        OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Test Core Functionality
+      continue-on-error: false
+      run: |
+        source .venv/bin/activate
+        
+        # Random sleep between 60-120 seconds to avoid API rate limits
+        SLEEP_TIME=$((RANDOM % 61 + 60))
+        echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
+        sleep $SLEEP_TIME
+        
+        echo "Testing core functionality..."
+        
+        # Test package imports
+        python3 -c "from praisonai.cli import PraisonAI; from praisonai.auto import AutoGenerator; from praisonai.agents_generator import AgentsGenerator" || exit 1
+        
+        # Test configuration loading
+        python3 -c "from praisonai.inc.config import generate_config; print(generate_config())" || exit 1
+        
+        # Test tools loading
+        python3 -c "from praisonai.inbuilt_tools import *" || exit 1
+        
+        echo "Core functionality tests passed!"
+      env:
+        OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        LOGLEVEL: "debug"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Test Agent Generation
+      continue-on-error: false
+      run: |
+        source .venv/bin/activate
+        
+        # Random sleep between 60-120 seconds to avoid API rate limits
+        SLEEP_TIME=$((RANDOM % 61 + 60))
+        echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
+        sleep $SLEEP_TIME
+        
+        echo "Testing agent generation..."
+        
+        # Create a test YAML file
+        cat > test_agents.yaml <<EOF
+        topic: "Test automation task"
+        framework: "${{ matrix.framework }} "
+        roles:
+          - name: "Tester"
+            goal: "Test the system"
+            backstory: "Expert system tester"
+            tasks:
+              - description: "Verify system functionality"
+                expected_output: "Test results"
+        EOF
+        
+        # Test YAML loading and agent generation
+        python3 -c '
+        from praisonai.agents_generator import AgentsGenerator
+        config = [{"model": "llama-3.3-70b-versatile", "base_url": "https://api.groq.com/openai/v1/"}]
+        generator = AgentsGenerator("test_agents.yaml", "${{ matrix.framework }} ", config)
+        print("Agent generation test passed!")
+        ' || exit 1
+        
+        echo "Agent generation tests passed!"
+      env:
+        OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        LOGLEVEL: "debug"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Test Custom Tools Integration
+      continue-on-error: false
+      run: |
+        source .venv/bin/activate
+        
+        # Random sleep between 60-120 seconds to avoid API rate limits
+        SLEEP_TIME=$((RANDOM % 61 + 60))
+        echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
+        sleep $SLEEP_TIME
+        
+        echo "Testing custom tools integration..."
+        
+        # Create a test tool
+        cat > test_tool.py <<EOF
+        from duckduckgo_search import DDGS
+        from praisonai_tools import BaseTool
+
+        class InternetSearchTool(BaseTool):
+            name: str = "Internet Search Tool"
+            description: str = "Search Internet for relevant information"
+
+            def _run(self, query: str):
+                return {"status": "success", "query": query}
+                
+            def test(self):
+                """Test method to verify tool setup"""
+                test_query = "test query"
+                result = self._run(test_query)
+                assert result["status"] == "success"
+                assert result["query"] == test_query
+                return True
+        EOF
+        
+        # Test tool loading and initialization
+        python3 -c '
+        import importlib.util
+        import sys
+        
+        # Load the test tool
+        spec = importlib.util.spec_from_file_location("test_tool", "test_tool.py")
+        module = importlib.util.module_from_spec(spec)
+        sys.modules["test_tool"] = module
+        spec.loader.exec_module(module)
+        
+        # Initialize and test the tool
+        tool = module.InternetSearchTool()
+        assert tool.test(), "Tool test failed"
+        print("Custom tool test passed!")
+        ' || exit 1
+        
+        echo "Custom tools integration test passed!"
+      env:
+        OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        LOGLEVEL: "debug"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Test PraisonAI Agents Configuration
+      continue-on-error: false
+      run: |
+        source .venv/bin/activate
+        
+        # Random sleep between 60-120 seconds to avoid API rate limits
+        SLEEP_TIME=$((RANDOM % 61 + 60))
+        echo "Sleeping for $SLEEP_TIME seconds to avoid API rate limits..."
+        sleep $SLEEP_TIME
+        
+        echo "Testing agents configuration..."
+        mkdir -p test_output || { echo "Failed to create test_output directory"; exit 1; }
+        
+        # Create test script
+        cat > test_agents.py <<EOF
+        from langchain_openai import ChatOpenAI
+        from crewai import Agent, Task, Crew
+        import os
+        import logging
+        import time
+        import random
+
+        logging.basicConfig(level="DEBUG")
+        logger = logging.getLogger(__name__)
+
+        def random_sleep(min_seconds=15, max_seconds=30):
+            """Sleep for a random duration to prevent API rate limiting"""
+            duration = random.uniform(min_seconds, max_seconds)
+            logger.info(f"Rate limit protection: Sleeping for {duration:.2f} seconds...")
+            time.sleep(duration)
+
+        class RateLimitedChatOpenAI(ChatOpenAI):
+            """ChatOpenAI with built-in rate limiting"""
+            def __call__(self, *args, **kwargs):
+                random_sleep()
+                return super().__call__(*args, **kwargs)
+
+        def run_test():
+            logger.info("Initializing test components...")
+            
+            # Initialize the LLM with rate limiting
+            llm = RateLimitedChatOpenAI(
+                model=os.getenv('OPENAI_MODEL_NAME', 'llama-3.3-70b-versatile'),
+                base_url=os.getenv('OPENAI_API_BASE', 'https://api.groq.com/openai/v1/'),
+                api_key=os.getenv('OPENAI_API_KEY'),
+                temperature=0.7
+            )
+
+            random_sleep()
+            logger.info("Creating researcher agent...")
+            researcher = Agent(
+                role="Test Automation Expert",
+                goal="Verify system functionality and performance",
+                backstory="""You are an expert in test automation and system verification,
+                skilled in identifying potential issues and validating functionality.""",
+                verbose=True,
+                allow_delegation=False,
+                llm=llm
+            )
+
+            random_sleep()
+            logger.info("Creating validator agent...")
+            validator = Agent(
+                role="Quality Assurance Specialist",
+                goal="Validate test results and ensure system reliability",
+                backstory="""You are a QA specialist focused on ensuring
+                system reliability and proper functionality.""",
+                verbose=True,
+                allow_delegation=False,
+                llm=llm
+            )
+
+            random_sleep()
+            logger.info("Creating test tasks...")
+            test_task = Task(
+                description="Run a basic system verification test",
+                expected_output="A report on system functionality verification",
+                agent=researcher
+            )
+
+            random_sleep()
+            validation_task = Task(
+                description="Validate the test results and compile findings",
+                expected_output="A validation report of test findings",
+                agent=validator
+            )
+
+            random_sleep()
+            logger.info("Initializing crew...")
+            crew = Crew(
+                agents=[researcher, validator],
+                tasks=[test_task, validation_task],
+                verbose=2
+            )
+
+            logger.info("Starting crew execution...")
+            result = crew.kickoff()
+            logger.info("Test execution completed.")
+            logger.info(f"Result: {result}")
+            return result
+
+        if __name__ == "__main__":
+            try:
+                run_test()
+            except Exception as e:
+                logger.error(f"Test failed with error: {str(e)}", exc_info=True)
+                raise
+        EOF
+        
+        # Run the test script with increased timeout
+        echo "Testing with Python API..."
+        timeout 1800 python3 test_agents.py 2>&1 | tee test_output/agents_test.log
+        
+        # Verify the output
+        if [[ ! -s test_output/agents_test.log ]]; then
+          echo "Error: No output generated from agents test"
+          exit 1
+        fi
+        
+        echo "Agents configuration tests passed!"
+      env:
+        OPENAI_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
+        OPENAI_MODEL_NAME: "llama-3.3-70b-versatile"
+        OPENAI_API_BASE: "https://api.groq.com/openai/v1/"
+        OPENAI_API_BASE_URL: "https://api.groq.com/openai/v1/"
+        LOGLEVEL: "debug"
+        PRAISONAI_NON_INTERACTIVE: "true"
+
+    - name: Upload test artifacts
+      if: always()
+      uses: actions/upload-artifact@v3
+      with:
+        name: praisonai-output-${{ matrix.os }}-${{ matrix.framework }}
+        path: test_output/
+        retention-days: 14
+
+  create-release:
+    needs: [test-ubuntu]
+    runs-on: ubuntu-latest
+    if: success()
+    steps:
+    # First checkout the fork's main branch
+    - name: Checkout fork repo
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+        token: ${{ secrets.PAT_TOKEN }}
+        repository: ${{ github.event.inputs.fork_owner }}/PraisonAI
+        ref: main
+
+    # Add upstream and get commit from tag
+    - name: Get upstream tag commit
+      id: get_upstream_commit
+      run: |
+        # Add upstream remote
+        git remote add upstream https://github.com/MervinPraison/PraisonAI.git
+        
+        # Delete local tag if it exists
+        git tag -d ${{ github.event.inputs.tag }} || true
+        
+        # Fetch from upstream including tags with force
+        git fetch upstream --tags --force
+        
+        # Verify tag exists in upstream
+        if ! git ls-remote --tags upstream | grep -q "refs/tags/${{ github.event.inputs.tag }}$"; then
+          echo "::error::Tag ${{ github.event.inputs.tag }} not found in upstream repository"
+          exit 1
+        fi
+        
+        # Get the commit hash from the upstream tag
+        UPSTREAM_COMMIT=$(git rev-list -n 1 refs/tags/${{ github.event.inputs.tag }})
+        if [ -z "$UPSTREAM_COMMIT" ]; then
+          echo "::error::Could not find commit for tag ${{ github.event.inputs.tag }}"
+          exit 1
+        fi
+        echo "upstream_commit=$UPSTREAM_COMMIT" >> $GITHUB_OUTPUT
+        echo "Found commit: $UPSTREAM_COMMIT for tag ${{ github.event.inputs.tag }}"
+
+    # Create tag at the same commit
+    - name: Create tag at commit
+      run: |
+        git config --global user.name "GitHub Actions"
+        git config --global user.email "[email protected]"
+        
+        # Remove tag if it exists locally
+        git tag -d ${{ github.event.inputs.tag }} || true
+        
+        # Remove tag if it exists remotely
+        git push origin :refs/tags/${{ github.event.inputs.tag }} || true
+        
+        # Fetch the specific commit from upstream
+        git fetch upstream ${{ steps.get_upstream_commit.outputs.upstream_commit }} --force
+        
+        # Reset to upstream commit
+        git reset --hard ${{ steps.get_upstream_commit.outputs.upstream_commit }}
+        
+        # Create and push tag
+        git tag -a ${{ github.event.inputs.tag }} -m "Release ${{ github.event.inputs.tag }} (synced from upstream MervinPraison/PraisonAI@${{ github.event.inputs.tag }})"
+        git push origin ${{ github.event.inputs.tag }}
+      env:
+        GIT_ASKPASS: /bin/echo
+        PAT_TOKEN: ${{ secrets.PAT_TOKEN }}
+
+    # Create the release
+    - name: Create Release
+      uses: actions/create-release@v1
+      env:
+        GITHUB_TOKEN: ${{ secrets.PAT_TOKEN }}
+      with:
+        tag_name: ${{ github.event.inputs.tag }}
+        release_name: Release ${{ github.event.inputs.tag }}
+        draft: false
+        prerelease: false
+        body: |
+          Release ${{ github.event.inputs.tag }} (synced from upstream)
+          
+          This release is synchronized with MervinPraison/PraisonAI@${{ github.event.inputs.tag }}
+          Commit: ${{ steps.get_upstream_commit.outputs.upstream_commit }}
+          
+          For installation:
+          ```bash
+          pip install praisonai
+          ```
+          
+          Changes from upstream v2.0.10:
+          - Updated agent configuration tests
+          - Added rate limiting protection
+          - Improved test reliability
+          
+          Forked from: MervinPraison/PraisonAI

.gitignore

@@ -64,4 +64,5 @@ node_modules
 outputs
 agents/praisonaiagents/build
 agents/praisonaiagents/dist
-agents/praisonaiagents/praisonaiagents.egg-info
+agents/praisonaiagents/praisonaiagents.egg-info
+.codegpt

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.6 gunicorn markdown
+RUN pip install flask praisonai==2.0.38 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

Dockerfile.chat

@@ -0,0 +1,24 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gcc \
+    python3-dev \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python packages
+RUN pip install --no-cache-dir \
+    praisonaiagents>=0.0.4 \
+    praisonai_tools \
+    "praisonai==2.0.18" \
+    "praisonai[chat]" \
+    "embedchain[github,youtube]"
+
+# Copy application code
+COPY . .
+
+# Default command (will be overridden by docker-compose)
+CMD ["praisonai", "chat"]

Dockerfile.dev

@@ -0,0 +1,28 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gcc \
+    python3-dev \
+    portaudio19-dev \
+    python3-pyaudio \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python packages
+RUN pip install --no-cache-dir \
+    praisonaiagents>=0.0.4 \
+    praisonai_tools \
+    "praisonai==2.0.18" \
+    "praisonai[ui]" \
+    "praisonai[chat]" \
+    "praisonai[realtime]" \
+    "embedchain[github,youtube]"
+
+# Copy application code
+COPY . .
+
+# Default command (will be overridden by docker-compose)
+CMD ["praisonai", "ui"]

Dockerfile.ui

@@ -0,0 +1,24 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gcc \
+    python3-dev \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python packages
+RUN pip install --no-cache-dir \
+    praisonaiagents>=0.0.4 \
+    praisonai_tools \
+    "praisonai==2.0.18" \
+    "praisonai[ui]" \
+    "praisonai[crewai]"
+
+# Copy application code
+COPY . .
+
+# Default command (will be overridden by docker-compose)
+CMD ["praisonai", "ui"]

README.md

@@ -20,7 +20,142 @@
 
 </div>
 
-Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralised framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.
+PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human–agent collaboration.
+
+<div align="center">
+  <a href="https://docs.praison.ai">
+    <p align="center">
+      <img src="https://img.shields.io/badge/📚_Documentation-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Documentation" />
+    </p>
+  </a>
+</div>
+
+## Key Features
+
+- 🤖 Automated AI Agents Creation
+- 🔄 Self Reflection AI Agents
+- 🧠 Reasoning AI Agents
+- 👁️ Multi Modal AI Agents
+- 🤝 Multi Agent Collaboration
+- ⚡ AI Agent Workflow
+- 🔄 Use CrewAI or AutoGen Framework
+- 💯 100+ LLM Support
+- 💻 Chat with ENTIRE Codebase
+- 🎨 Interactive UIs
+- 📄 YAML-based Configuration
+- 🛠️ Custom Tool Integration
+- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
+- 🖼️ Vision Language Model (VLM) Support
+- 🎙️ Real-time Voice Interaction
+
+## Using No Code
+
+### Auto Mode:
+```bash
+pip install praisonai
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --auto create a movie script about Robots in Mars
+```
+
+### Initialise Mode:
+```bash
+pip install praisonai
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --init create a movie script about Robots in Mars
+praisonai
+```
+
+## Using Coding
+
+Light weight package dedicated for coding:
+```bash
+pip install praisonaiagents
+```
+
+```bash
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+```
+
+Create app.py file and add the code below:
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# 1. Create agents
+researcher = Agent(
+    name="Researcher",
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI and data science",
+    backstory="""You are an expert at a technology research group, 
+    skilled in identifying trends and analyzing complex data.""",
+    verbose=True,
+    llm="gpt-4o",
+    markdown=True
+)
+writer = Agent(
+    name="Writer",
+    role="Tech Content Strategist",
+    goal="Craft compelling content on tech advancements",
+    backstory="""You are a content strategist known for 
+    making complex tech topics interesting and easy to understand.""",
+    llm="gpt-4o",
+    markdown=True
+)
+
+# 2. Define Tasks
+task1 = Task(
+    name="research_task",
+    description="""Analyze 2024's AI advancements. 
+    Find major trends, new technologies, and their effects.""",
+    expected_output="""A detailed report on 2024 AI advancements""",
+    agent=researcher
+)
+
+task2 = Task(
+    name="writing_task",
+    description="""Create a blog post about major AI advancements using the insights you have.
+    Make it interesting, clear, and suited for tech enthusiasts. 
+    It should be at least 4 paragraphs long.""",
+    expected_output="A blog post of at least 4 paragraphs",
+    agent=writer,
+)
+
+agents = PraisonAIAgents(
+    agents=[researcher, writer],
+    tasks=[task1, task2],
+    verbose=False,
+    process="hierarchical",
+    manager_llm="gpt-4o"
+)
+
+result = agents.start()
+```
+
+Run:
+```bash
+python app.py
+```
+
+## Ollama Integration
+```bash
+export OPENAI_BASE_URL=http://localhost:11434/v1
+```
+
+## Groq Integration
+Replace xxxx with Groq API KEY:
+```bash
+export OPENAI_API_KEY=xxxxxxxxxxx
+export OPENAI_BASE_URL=https://api.groq.com/openai/v1
+```
+
+## Logging
+```bash
+export LOGLEVEL=info
+```
+
+Advanced logging:
+```bash
+export LOGLEVEL=debug
+```
 
 <div align="center">
   <picture>
@@ -130,18 +265,7 @@ When installing with `pip install "praisonai[autogen]"`, you get:
 - Multi-agent conversation capabilities
 - Code execution environment
 
-## Key Features
 
-- 🤖 Automated AI Agents Creation
-- 🔄 Use CrewAI or AutoGen Framework
-- 💯 100+ LLM Support
-- 💻 Chat with ENTIRE Codebase
-- 🖥️ Interactive UIs
-- 📄 YAML-based Configuration
-- 🛠️ Custom Tool Integration
-- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
-- 👁️ Vision Language Model (VLM) Support
-- 🎙️ Real-time Voice Interaction
 
 ## TL;DR Multi Agents
 
@@ -353,33 +477,7 @@ if __name__ == "__main__":
 
 ## Commands to Install Dependencies:
 
-1. **Install all dependencies, including dev dependencies:**
-
-   ```sh
-   poetry install
-   ```
-
-2. **Install only documentation dependencies:**
-
-   ```sh
-   poetry install --with docs
-   ```
-
-3. **Install only test dependencies:**
-
-   ```sh
-   poetry install --with test
-   ```
-
-4. **Install only dev dependencies:**
-
-   ```sh
-   poetry install --with dev
-   ```
-
-This configuration ensures that your development dependencies are correctly categorized and installed as needed.
-
-### Using uv (Fast Python Package Installer)
+### Using uv
 ```bash
 # Install uv if you haven't already
 pip install uv
@@ -430,3 +528,55 @@ Praison AI is an open-sourced software licensed under the **[MIT license](https:
 ## License
 
 Praison AI is an open-sourced software licensed under the **[MIT license](https://opensource.org/licenses/MIT)**.
+
+## Local Docker Development with Live Reload
+
+To facilitate local development with live reload, you can use Docker. Follow the steps below:
+
+1. **Create a `Dockerfile.dev`**:
+    ```dockerfile
+    FROM python:3.11-slim
+
+    WORKDIR /app
+
+    COPY . .
+
+    RUN pip install flask praisonai==2.0.18 watchdog
+
+    EXPOSE 5555
+
+    ENV FLASK_ENV=development
+
+    CMD ["flask", "run", "--host=0.0.0.0"]
+    ```
+
+2. **Create a `docker-compose.yml`**:
+    ```yaml
+    version: '3.8'
+
+    services:
+      app:
+        build:
+          context: .
+          dockerfile: Dockerfile.dev
+        volumes:
+          - .:/app
+        ports:
+          - "5555:5555"
+        environment:
+          FLASK_ENV: development
+        command: flask run --host=0.0.0.0
+
+      watch:
+        image: alpine:latest
+        volumes:
+          - .:/app
+        command: sh -c "apk add --no-cache inotify-tools && while inotifywait -r -e modify,create,delete /app; do kill -HUP 1; done"
+    ```
+
+3. **Run Docker Compose**:
+    ```bash
+    docker-compose up
+    ```
+
+This setup will allow you to develop locally with live reload, making it easier to test and iterate on your code.

agents.yaml

@@ -1,5 +1,5 @@
 framework: praisonai
-process: hierarchical
+process: sequential
 topic: create movie script about cat in mars
 roles:
   researcher:
@@ -7,13 +7,15 @@ roles:
       information.
     goal: Gather information about Mars and cats
     role: Researcher
+    min_reflect: 1
+    max_reflect: 2
     tasks:
       research_task:
         description: Research about Mars, its environment, and the feasibility of
           a cat being on Mars. Also, research about cat behavior and characteristics.
         expected_output: Document with research findings on Mars and cats.
     tools:
-    - ''
+    - search_tool
   narrative_designer:
     backstory: Skilled in narrative development, with a focus on creating engaging
       stories.
@@ -38,4 +40,4 @@ roles:
         expected_output: Final movie script with dialogue and scene details.
     tools:
     - ''
-dependencies: []
+dependencies: []

agents/.cursorrules

@@ -0,0 +1,8 @@
+1. Make minimal code changes as possible
+2. if you are creating function, make sure to name it in more easy, user friendly , optimised manner. 
+3. Never copy the exact code if presented with a code from other location or file or codebase, just make it optimised
+4. Always Follow the principle LESS IS MORE
+5. Make it minimal change as possible
+6. Firstly try not to make any modification to the existing code as possible
+7. Only modify the existing code if its highly required, without that if it cant be done, then add new code section. 
+8. If you are adding new code, make sure to add it in a way that it can be easily integrated with the existing codebase. 

agents/.gitignore

@@ -81,3 +81,7 @@ praisonaiagents/dist
 praisonaiagents/praisonaiagents.egg-info
 dist
 build
+*.mp4 
+*.png 
+graph.py
+chroma_db/

agents/.praisonignore

@@ -0,0 +1,32 @@
+.cursorrules
+.gitignore
+.praisoncontext
+.praisonignore
+.windsurfrules
+async_example.py
+async_example_full.py
+async_example_full_multigroups.py
+auto_agents_example.py
+code_agents_example.py
+example_callback.py
+example_custom_tools.py
+example_sequential.py
+image.jpg
+langchain_example.py
+langchain_logs.py
+long_term.db
+mini_agents_example.py
+multimodal.py
+praisonaiagents/tools/*
+praisonaiagents/process/*
+pyproject.toml
+short_term.db
+structured_agents_example.py
+structured_response_example.py
+test.py
+tools_example.py
+uv.lock
+workflow_example_basic.py
+workflow_example_detailed.py
+__pycache__
+__pycache__/*

agents/.windsurfrules

@@ -0,0 +1,8 @@
+1. Make minimal code changes as possible
+2. if you are creating function, make sure to name it in more easy, user friendly , optimised manner. 
+3. Never copy the exact code if presented with a code from other location or file or codebase, just make it optimised
+4. Always Follow the principle LESS IS MORE
+5. Make it minimal change as possible
+6. Firstly try not to make any modification to the existing code as possible
+7. Only modify the existing code if its highly required, without that if it cant be done, then add new code section. 
+8. If you are adding new code, make sure to add it in a way that it can be easily integrated with the existing codebase. 

agents/async_example.py

@@ -0,0 +1,233 @@
+import asyncio
+import time
+from typing import List, Dict
+from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+from praisonaiagents.main import (
+    display_error,
+    display_interaction,
+    display_tool_call,
+    display_instruction,
+    error_logs,
+    Console
+)
+from duckduckgo_search import DDGS
+from pydantic import BaseModel
+
+console = Console()
+
+# 1. Define output model for structured results
+class SearchResult(BaseModel):
+    query: str
+    results: List[Dict[str, str]]
+    total_results: int
+
+# 2. Define both sync and async tools
+def sync_search_tool(query: str) -> List[Dict]:
+    """
+    Synchronous search using DuckDuckGo.
+    Args:
+        query (str): The search query.
+    Returns:
+        list: Search results
+    """
+    display_tool_call(f"Running sync search for: {query}", console)
+    time.sleep(1)  # Simulate network delay
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=5):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+    except Exception as e:
+        error_msg = f"Error during sync search: {e}"
+        display_error(error_msg, console)
+        error_logs.append(error_msg)
+        return []
+
+async def async_search_tool(query: str) -> List[Dict]:
+    """
+    Asynchronous search using DuckDuckGo.
+    Args:
+        query (str): The search query.
+    Returns:
+        list: Search results
+    """
+    display_tool_call(f"Running async search for: {query}", console)
+    await asyncio.sleep(1)  # Simulate network delay
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=5):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+    except Exception as e:
+        error_msg = f"Error during async search: {e}"
+        display_error(error_msg, console)
+        error_logs.append(error_msg)
+        return []
+
+# 3. Define both sync and async callbacks
+def sync_callback(output: TaskOutput):
+    display_interaction("Sync Callback", f"Processing output: {output.raw[:100]}...", markdown=True, console=console)
+    time.sleep(1)  # Simulate processing
+    if output.output_format == "JSON":
+        display_tool_call(f"Processed JSON result: {output.json_dict}", console)
+    elif output.output_format == "Pydantic":
+        display_tool_call(f"Processed Pydantic result: {output.pydantic}", console)
+
+async def async_callback(output: TaskOutput):
+    display_interaction("Async Callback", f"Processing output: {output.raw[:100]}...", markdown=True, console=console)
+    await asyncio.sleep(1)  # Simulate processing
+    if output.output_format == "JSON":
+        display_tool_call(f"Processed JSON result: {output.json_dict}", console)
+    elif output.output_format == "Pydantic":
+        display_tool_call(f"Processed Pydantic result: {output.pydantic}", console)
+
+# 4. Create agents with different tools
+sync_agent = Agent(
+    name="SyncAgent",
+    role="Synchronous Search Specialist",
+    goal="Perform synchronous searches and return structured results",
+    backstory="Expert in sync operations and data organization",
+    tools=[sync_search_tool],
+    self_reflect=False,
+    verbose=True,
+    markdown=True
+)
+
+async_agent = Agent(
+    name="AsyncAgent",
+    role="Asynchronous Search Specialist",
+    goal="Perform asynchronous searches and return structured results",
+    backstory="Expert in async operations and data organization",
+    tools=[async_search_tool],
+    self_reflect=False,
+    verbose=True,
+    markdown=True
+)
+
+# 5. Create tasks with different configurations
+sync_task = Task(
+    name="sync_search",
+    description="Search for 'Python programming' using sync tool and return structured results",
+    expected_output="SearchResult model with query details and results",
+    agent=sync_agent,
+    async_execution=False,
+    callback=sync_callback,
+    output_pydantic=SearchResult
+)
+
+async_task = Task(
+    name="async_search",
+    description="Search for 'Async programming' using async tool and return structured results",
+    expected_output="SearchResult model with query details and results",
+    agent=async_agent,
+    async_execution=True,
+    callback=async_callback,
+    output_pydantic=SearchResult
+)
+
+# 6. Create workflow tasks
+workflow_sync_task = Task(
+    name="workflow_sync",
+    description="Workflow sync search for 'AI trends' with structured output",
+    expected_output="SearchResult model with AI trends data",
+    agent=sync_agent,
+    async_execution=False,
+    is_start=True,
+    next_tasks=["workflow_async"],
+    output_pydantic=SearchResult
+)
+
+workflow_async_task = Task(
+    name="workflow_async",
+    description="Workflow async search for 'Future of AI' with structured output",
+    expected_output="SearchResult model with Future of AI data",
+    agent=async_agent,
+    async_execution=True,
+    output_pydantic=SearchResult
+)
+
+# 7. Example usage functions
+def run_sync_example():
+    """Run synchronous example"""
+    display_instruction("\nRunning Synchronous Example...", console)
+    agents = PraisonAIAgents(
+        agents=[sync_agent],
+        tasks=[sync_task],
+        verbose=1,
+        process="sequential"
+    )
+    result = agents.start()
+    display_interaction("Sync Example", f"Result: {result}", markdown=True, console=console)
+
+async def run_async_example():
+    """Run asynchronous example"""
+    display_instruction("\nRunning Asynchronous Example...", console)
+    agents = PraisonAIAgents(
+        agents=[async_agent],
+        tasks=[async_task],
+        verbose=1,
+        process="sequential"
+    )
+    result = await agents.astart()
+    display_interaction("Async Example", f"Result: {result}", markdown=True, console=console)
+
+async def run_mixed_example():
+    """Run mixed sync/async example"""
+    display_instruction("\nRunning Mixed Sync/Async Example...", console)
+    agents = PraisonAIAgents(
+        agents=[sync_agent, async_agent],
+        tasks=[sync_task, async_task],
+        verbose=1,
+        process="sequential"
+    )
+    result = await agents.astart()
+    display_interaction("Mixed Example", f"Result: {result}", markdown=True, console=console)
+
+async def run_workflow_example():
+    """Run workflow example with both sync and async tasks"""
+    display_instruction("\nRunning Workflow Example...", console)
+    agents = PraisonAIAgents(
+        agents=[sync_agent, async_agent],
+        tasks=[workflow_sync_task, workflow_async_task],
+        verbose=1,
+        process="workflow"
+    )
+    result = await agents.astart()
+    display_interaction("Workflow Example", f"Result: {result}", markdown=True, console=console)
+
+# 8. Main execution
+async def main():
+    """Main execution function"""
+    display_instruction("Starting PraisonAI Agents Examples...", console)
+    
+    try:
+        # Run sync example in a separate thread to not block the event loop
+        loop = asyncio.get_event_loop()
+        await loop.run_in_executor(None, run_sync_example)
+        
+        # Run async examples
+        await run_async_example()
+        await run_mixed_example()
+        await run_workflow_example()
+
+        if error_logs:
+            display_error("\nErrors encountered during execution:", console)
+            for error in error_logs:
+                display_error(error, console)
+    except Exception as e:
+        display_error(f"Error in main execution: {e}", console)
+        error_logs.append(str(e))
+
+if __name__ == "__main__":
+    # Run the main function
+    asyncio.run(main()) 

agents/async_example_full.py

@@ -0,0 +1,152 @@
+import asyncio
+import time
+from typing import List, Dict
+from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+from duckduckgo_search import DDGS
+from pydantic import BaseModel
+
+# 1. Define output model for structured results
+class SearchResult(BaseModel):
+    query: str
+    results: List[Dict[str, str]]
+    total_results: int
+
+# 2. Define async tool
+async def async_search_tool(query: str) -> Dict:
+    """Perform asynchronous search and return structured results."""
+    await asyncio.sleep(1)  # Simulate network delay
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=5):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        
+        return {
+            "query": query,
+            "results": results,
+            "total_results": len(results)
+        }
+    except Exception as e:
+        print(f"Error during async search: {e}")
+        return {
+            "query": query,
+            "results": [],
+            "total_results": 0
+        }
+
+# 3. Define async callback
+async def async_callback(output: TaskOutput):
+    await asyncio.sleep(1)  # Simulate processing
+    if output.output_format == "JSON":
+        print(f"Processed JSON result: {output.json_dict}")
+    elif output.output_format == "Pydantic":
+        print(f"Processed Pydantic result: {output.pydantic}")
+
+# 4. Create specialized agents
+async_agent = Agent(
+    name="AsyncSearchAgent",
+    role="Search Specialist",
+    goal="Perform fast parallel searches with structured results",
+    backstory="Expert in efficient data retrieval and parallel search operations",
+    tools=[async_search_tool],
+    self_reflect=False,
+    verbose=True,
+    markdown=True
+)
+
+summary_agent = Agent(
+    name="SummaryAgent",
+    role="Research Synthesizer",
+    goal="Create concise summaries from multiple search results",
+    backstory="Expert in analyzing and synthesizing information from multiple sources",
+    self_reflect=True,
+    verbose=True,
+    markdown=True
+)
+
+# 5. Create async tasks
+async_task = Task(
+    name="async_search",
+    description="Search for 'Async programming' and return results in JSON format with query, results array, and total_results count.",
+    expected_output="SearchResult model with structured data",
+    agent=async_agent,
+    async_execution=True,
+    callback=async_callback,
+    output_json=SearchResult
+)
+
+async def run_parallel_tasks():
+    """Run multiple async tasks in parallel"""
+    print("\nRunning Parallel Async Tasks...")
+    
+    # Define different search topics
+    search_topics = [
+        "Latest AI Developments 2024",
+        "Machine Learning Best Practices",
+        "Neural Networks Architecture"
+    ]
+    
+    # Create tasks for different topics
+    parallel_tasks = [
+        Task(
+            name=f"search_task_{i}",
+            description=f"Search for '{topic}' and return structured results with query details and findings.",
+            expected_output="SearchResult model with search data",
+            agent=async_agent,
+            async_execution=True,
+            callback=async_callback,
+            output_json=SearchResult
+        ) for i, topic in enumerate(search_topics)
+    ]
+    
+    # Create summarization task
+    summary_task = Task(
+        name="summary_task",
+        description="Analyze all search results and create a concise summary highlighting key findings, patterns, and implications.",
+        expected_output="Structured summary with key findings and insights",
+        agent=summary_agent,
+        async_execution=False,
+        callback=async_callback,
+        context=parallel_tasks
+    )
+    
+    # Create a single PraisonAIAgents instance with both agents
+    agents = PraisonAIAgents(
+        agents=[async_agent, summary_agent],
+        tasks=parallel_tasks + [summary_task],
+        verbose=1,
+        process="sequential"
+    )
+    
+    # Run all tasks
+    results = await agents.astart()
+    print(f"Tasks Results: {results}")
+    
+    # Return results in a serializable format
+    return {
+        "search_results": {
+            "task_status": {k: v for k, v in results["task_status"].items() if k != summary_task.id},
+            "task_results": [str(results["task_results"][i]) if results["task_results"][i] else None 
+                           for i in range(len(parallel_tasks))]
+        },
+        "summary": str(results["task_results"][summary_task.id]) if results["task_results"].get(summary_task.id) else None,
+        "topics": search_topics
+    }
+
+# 6. Main execution
+async def main():
+    """Main execution function"""
+    print("Starting Async AI Agents Examples...")
+    
+    try:
+        await run_parallel_tasks()
+    except Exception as e:
+        print(f"Error in main execution: {e}")
+
+if __name__ == "__main__":
+    # Run the main function
+    asyncio.run(main())

agents/async_example_full_multigroups.py

@@ -0,0 +1,248 @@
+import asyncio
+import time
+from typing import List, Dict
+from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+from duckduckgo_search import DDGS
+from pydantic import BaseModel
+
+# 1. Define output model for structured results
+class SearchResult(BaseModel):
+    query: str
+    results: List[Dict[str, str]]
+    total_results: int
+
+# 2. Define async tool
+async def async_search_tool(query: str) -> Dict:
+    """
+    Asynchronous search using DuckDuckGo.
+    Args:
+        query (str): The search query.
+    Returns:
+        dict: Search results in SearchResult model format
+    """
+    await asyncio.sleep(1)  # Simulate network delay
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=5):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        
+        # Format response to match SearchResult model
+        return {
+            "query": query,
+            "results": results,
+            "total_results": len(results)
+        }
+    except Exception as e:
+        print(f"Error during async search: {e}")
+        return {
+            "query": query,
+            "results": [],
+            "total_results": 0
+        }
+
+# 3. Define async callback
+async def async_callback(output: TaskOutput):
+    await asyncio.sleep(1)  # Simulate processing
+    if output.output_format == "JSON":
+        print(f"Processed JSON result: {output.json_dict}")
+    elif output.output_format == "Pydantic":
+        print(f"Processed Pydantic result: {output.pydantic}")
+
+# 4. Create specialized agents
+async_agent = Agent(
+    name="AsyncSearchAgent",
+    role="Asynchronous Search Specialist",
+    goal="Perform fast and efficient asynchronous searches with structured results",
+    backstory="Expert in parallel search operations and data retrieval",
+    tools=[async_search_tool],
+    self_reflect=False,
+    verbose=True,
+    markdown=True
+)
+
+summary_agent = Agent(
+    name="SummaryAgent",
+    role="Research Synthesizer",
+    goal="Create comprehensive summaries and identify patterns across multiple search results",
+    backstory="""Expert in analyzing and synthesizing information from multiple sources.
+Skilled at identifying patterns, trends, and connections between different topics.
+Specializes in creating clear, structured summaries that highlight key insights.""",
+    self_reflect=True,  # Enable self-reflection for better summary quality
+    verbose=True,
+    markdown=True
+)
+
+# 5. Create async tasks
+async_task = Task(
+    name="async_search",
+    description="""Search for 'Async programming' and return results in the following JSON format:
+{
+    "query": "the search query",
+    "results": [
+        {
+            "title": "result title",
+            "url": "result url",
+            "snippet": "result snippet"
+        }
+    ],
+    "total_results": number of results
+}""",
+    expected_output="SearchResult model with query details and results",
+    agent=async_agent,
+    async_execution=True,
+    callback=async_callback,
+    output_json=SearchResult
+)
+
+# 6. Example usage functions
+async def run_single_task():
+    """Run single async task"""
+    print("\nRunning Single Async Task...")
+    agents = PraisonAIAgents(
+        agents=[async_agent],
+        tasks=[async_task],
+        verbose=1,
+        process="sequential"
+    )
+    result = await agents.astart()
+    print(f"Single Task Result: {result}")
+
+async def run_parallel_tasks():
+    """Run multiple async tasks in parallel"""
+    print("\nRunning Parallel Async Tasks...")
+    
+    # Define different search topics
+    search_topics = [
+        "Latest AI Developments 2024",
+        "Machine Learning Best Practices",
+        "Neural Networks Architecture"
+    ]
+    
+    # Create tasks for different topics
+    parallel_tasks = [
+        Task(
+            name=f"search_task_{i}",
+            description=f"""Search for '{topic}' and return results in the following JSON format:
+{{
+    "query": "{topic}",
+    "results": [
+        {{
+            "title": "result title",
+            "url": "result url",
+            "snippet": "result snippet"
+        }}
+    ],
+    "total_results": number of results
+}}""",
+            expected_output="SearchResult model with detailed information",
+            agent=async_agent,
+            async_execution=True,
+            callback=async_callback,
+            output_json=SearchResult
+        ) for i, topic in enumerate(search_topics)
+    ]
+    
+    # Create summarization task with the specialized summary agent
+    summary_task = Task(
+        name="summary_task",
+        description="""As a Research Synthesizer, analyze the search results and create a comprehensive summary. Your task:
+
+1. Analyze Results:
+   - Review all search results thoroughly
+   - Extract key findings from each topic
+   - Identify main themes and concepts
+
+2. Find Connections:
+   - Identify relationships between topics
+   - Spot common patterns or contradictions
+   - Note emerging trends across sources
+
+3. Create Structured Summary:
+   - Main findings per topic
+   - Cross-cutting themes
+   - Emerging trends
+   - Practical implications
+   - Future directions
+
+4. Quality Checks:
+   - Ensure all topics are covered
+   - Verify accuracy of connections
+   - Confirm clarity of insights
+   - Validate practical relevance
+
+Present the summary in a clear, structured format with sections for findings, patterns, trends, and implications.""",
+        expected_output="""A comprehensive research synthesis containing:
+- Detailed findings from each search topic
+- Cross-topic patterns and relationships
+- Emerging trends and their implications
+- Practical applications and future directions""",
+        agent=summary_agent,  # Use the specialized summary agent
+        async_execution=False,  # Run synchronously after search tasks
+        callback=async_callback
+    )
+    
+    # First run parallel search tasks
+    agents = PraisonAIAgents(
+        agents=[async_agent],
+        tasks=parallel_tasks,  # Only run search tasks first
+        verbose=1,
+        process="sequential"
+    )
+    search_results = await agents.astart()
+    print(f"Search Tasks Results: {search_results}")
+    
+    # Create task objects with results for context
+    completed_tasks = []
+    for i, topic in enumerate(search_topics):
+        task = Task(
+            name=f"search_task_{i}_result",
+            description=f"Search results for: {topic}",
+            expected_output="Search results from previous task",
+            agent=async_agent,
+            result=search_results["task_results"][i]
+        )
+        completed_tasks.append(task)
+    
+    # Update summary task with context from search results
+    summary_task.context = completed_tasks
+    
+    # Run summarization task with summary agent
+    summary_agents = PraisonAIAgents(
+        agents=[summary_agent],  # Use summary agent for synthesis
+        tasks=[summary_task],
+        verbose=1,
+        process="sequential"
+    )
+    summary_result = await summary_agents.astart()
+    print(f"Summary Task Result: {summary_result}")
+    
+    # Return results in a serializable format
+    return {
+        "search_results": {
+            "task_status": search_results["task_status"],
+            "task_results": [str(result) if result else None for result in search_results["task_results"]]
+        },
+        "summary": str(summary_result),
+        "topics": search_topics
+    }
+
+# 7. Main execution
+async def main():
+    """Main execution function"""
+    print("Starting Async AI Agents Examples...")
+    
+    try:
+        # Run different async patterns
+        await run_single_task()
+        await run_parallel_tasks()
+    except Exception as e:
+        print(f"Error in main execution: {e}")
+
+if __name__ == "__main__":
+    # Run the main function
+    asyncio.run(main())

agents/auto_agents_example.py

@@ -0,0 +1,11 @@
+from praisonaiagents import AutoAgents
+from praisonaiagents.tools import duckduckgo
+
+agents = AutoAgents(
+    instructions="Search for information about AI Agents",
+    tools=[duckduckgo],
+    process="sequential",
+    verbose=True
+)
+
+agents.start()

agents/code_agents_example.py

@@ -0,0 +1,50 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import json
+from e2b_code_interpreter import Sandbox
+
+def code_interpreter(code: str):
+    """
+    A function to demonstrate running Python code dynamically using e2b_code_interpreter.
+    """
+    print(f"\n{'='*50}\n> Running following AI-generated code:\n{code}\n{'='*50}")
+    exec_result = Sandbox().run_code(code)
+    if exec_result.error:
+        print("[Code Interpreter error]", exec_result.error)
+        return {"error": str(exec_result.error)}
+    else:
+        results = []
+        for result in exec_result.results:
+            if hasattr(result, '__iter__'):
+                results.extend(list(result))
+            else:
+                results.append(str(result))
+        logs = {"stdout": list(exec_result.logs.stdout), "stderr": list(exec_result.logs.stderr)}
+        return json.dumps({"results": results, "logs": logs})
+
+code_agent = Agent(
+    name="code_agent",
+    llm="gpt-4o-mini",
+    backstory="Expert in writing Python scripts",
+    self_reflect=False
+)
+execution_agent = Agent(
+    name="execution_agent",
+    llm="gpt-4o-mini",
+    backstory="Expert in executing Python scripts",
+    self_reflect=False,
+    tools=[code_interpreter]
+)
+
+code_agent_task = Task(
+    description="Write a simple Python script to print 'Hello, World!'",
+    expected_output="A Python script that prints 'Hello, World!'",
+    agent=code_agent
+)
+execution_agent_task = Task(
+    description="Execute the Python script",
+    expected_output="The output of the Python script",
+    agent=execution_agent
+)
+
+agents = PraisonAIAgents(agents=[code_agent, execution_agent], tasks=[code_agent_task, execution_agent_task])
+agents.start()

agents/example.py

@@ -1,166 +0,0 @@
-import os
-import logging
-from rich.console import Console
-from rich.panel import Panel
-from rich.text import Text
-from praisonaiagents import Agent, Task, Agents, error_logs
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s %(filename)s:%(lineno)d %(levelname)s %(message)s",
-    datefmt="[%X]"
-)
-
-def my_callback(output):
-    logging.info(f"Callback function called after task execution: {output.description}")
-    logging.info(f"Task output: {output}")
-
-def main():
-    # Make sure OPENAI_API_KEY is set
-    if not os.environ.get("OPENAI_API_KEY"):
-        raise ValueError("Please set OPENAI_API_KEY environment variable")
-
-    # Define tools
-    search_tool = {
-        "type": "function",
-        "function": {
-            "name": "search_tool",
-            "description": "Use this to perform search queries",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "query": {"type": "string"},
-                },
-                "required": ["query"],
-            },
-        },
-    }
-    get_weather_tool = {
-        "type": "function",
-        "function": {
-            "name": "get_weather",
-            "description": "Get the weather for a location",
-            "parameters": {
-                "type": "object",
-                "properties": {
-                    "location": {"type": "string"},
-                },
-                "required": ["location"],
-            },
-        },
-    }
-
-    # Create agents
-    researcher = Agent(
-        name="Researcher",
-        role="Senior Research Analyst",
-        goal="Uncover cutting-edge developments in AI and data science",
-        backstory="""You are an expert at a technology research group, 
-        skilled in identifying trends and analyzing complex data.""",
-        verbose=True,
-        allow_delegation=False,
-        tools=[search_tool],
-        llm="gpt-4o",
-        markdown=True
-    )
-    writer = Agent(
-        name="Writer",
-        role="Tech Content Strategist",
-        goal="Craft compelling content on tech advancements",
-        backstory="""You are a content strategist known for 
-        making complex tech topics interesting and easy to understand.""",
-        verbose=True,
-        allow_delegation=True,
-        llm="gpt-4o",
-        tools=[get_weather_tool],
-        markdown=True
-    )
-
-    # Create tasks
-    task1 = Task(
-        name="research_task",
-        description="""Analyze 2024's AI advancements. 
-        Find major trends, new technologies, and their effects.""",
-        expected_output="""A detailed report on 2024 AI advancements""",
-        agent=researcher,
-        tools=[search_tool]
-    )
-
-    task2 = Task(
-        name="writing_task",
-        description="""Create a blog post about major AI advancements using the insights you have.
-        Make it interesting, clear, and suited for tech enthusiasts. 
-        It should be at least 4 paragraphs long. 
-        Also, call the get_weather tool to get the weather in Paris.""",
-        expected_output="A blog post of at least 4 paragraphs, and weather in Paris",
-        agent=writer,
-        context=[task1],
-        callback=my_callback,
-        tools=[get_weather_tool]
-    )
-
-    task3 = Task(
-        name="json_task",
-        description="""Create a json object with a title of "My Task" and content of "My content".""",
-        expected_output="""JSON output with title and content""",
-        agent=researcher,
-    )
-
-    task4 = Task(
-        name="save_output_task",
-        description="""Save the AI blog post to a file""",
-        expected_output="""File saved successfully""",
-        agent=writer,
-        context=[task2],
-        output_file='outputs/ai_blog_post.txt',
-        create_directory=True
-    )
-
-    # Create and run agents manager
-    agents = Agents(
-        agents=[researcher, writer],
-        tasks=[task1, task2, task3, task4],
-        verbose=False,
-        process="sequential",  # "sequential" or "hierarchical"
-        manager_llm="gpt-4o"
-    )
-
-    result = agents.start()
-
-    # Print results and error summary
-    console = Console()
-    
-    # Print task results
-    console.print("\n=== Task Results ===")
-    for task_id, task_status in result['task_status'].items():
-        console.print(f"Task {task_id}: {task_status}")
-        if task_result := result['task_results'].get(task_id):
-            console.print(f"Output: {task_result.raw[:200]}...")  # Show first 200 chars
-
-    # Print task details
-    console.print("\n=== Task Details ===")
-    for i in range(4):
-        console.print(agents.get_task_details(i))
-
-    # Print agent details
-    console.print("\n=== Agent Details ===")
-    console.print(agents.get_agent_details('Researcher'))
-    console.print(agents.get_agent_details('Writer'))
-
-    # Print any errors
-    if error_logs:
-        console.print(Panel.fit(Text("Errors Encountered:", style="bold red"), title="Error Summary", border_style="red"))
-        for err in error_logs:
-            console.print(f"- {err}")
-            if "parsing self-reflection json" in err:
-                console.print("  Reason: The self-reflection JSON response was not valid JSON.")
-            elif "Error: Task with ID" in err:
-                console.print("  Reason: Task ID referenced does not exist.")
-            elif "saving task output to file" in err:
-                console.print("  Reason: Possible file permissions or invalid path.")
-            else:
-                console.print("  Reason not identified")
-
-if __name__ == "__main__":
-    main() 

agents/example_callback.py

@@ -0,0 +1,204 @@
+from praisonaiagents import (
+    Agent, 
+    Task, 
+    PraisonAIAgents, 
+    error_logs, 
+    register_display_callback,
+    sync_display_callbacks,
+    async_display_callbacks
+)
+from duckduckgo_search import DDGS
+from rich.console import Console
+import json
+from datetime import datetime
+import logging
+
+# Setup logging
+logging.basicConfig(
+    filename='ai_interactions.log',
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+
+# Callback functions for different display types
+def interaction_callback(message=None, response=None, markdown=None, generation_time=None):
+    """Callback for display_interaction"""
+    logging.info(f"""
+    === INTERACTION ===
+    Time: {datetime.now()}
+    Generation Time: {generation_time}s
+    Message: {message}
+    Response: {response}
+    Markdown: {markdown}
+    """)
+
+def error_callback(message=None):
+    """Callback for display_error"""
+    logging.error(f"""
+    === ERROR ===
+    Time: {datetime.now()}
+    Message: {message}
+    """)
+
+def tool_call_callback(message=None):
+    """Callback for display_tool_call"""
+    logging.info(f"""
+    === TOOL CALL ===
+    Time: {datetime.now()}
+    Message: {message}
+    """)
+
+def instruction_callback(message=None):
+    """Callback for display_instruction"""
+    logging.info(f"""
+    === INSTRUCTION ===
+    Time: {datetime.now()}
+    Message: {message}
+    """)
+
+def self_reflection_callback(message=None):
+    """Callback for display_self_reflection"""
+    logging.info(f"""
+    === SELF REFLECTION ===
+    Time: {datetime.now()}
+    Message: {message}
+    """)
+
+def generating_callback(content=None, elapsed_time=None):
+    """Callback for display_generating"""
+    logging.info(f"""
+    === GENERATING ===
+    Time: {datetime.now()}
+    Content: {content}
+    Elapsed Time: {elapsed_time}
+    """)
+
+# Register all callbacks
+register_display_callback('interaction', interaction_callback)
+register_display_callback('error', error_callback)
+register_display_callback('tool_call', tool_call_callback)
+register_display_callback('instruction', instruction_callback)
+register_display_callback('self_reflection', self_reflection_callback)
+# register_display_callback('generating', generating_callback)
+
+def task_callback(output):
+    """Callback for task completion"""
+    logging.info(f"""
+    === TASK COMPLETED ===
+    Time: {datetime.now()}
+    Description: {output.description}
+    Agent: {output.agent}
+    Output: {output.raw[:200]}...
+    """)
+
+def internet_search_tool(query) -> list:
+    """
+    Perform a search using DuckDuckGo.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        list: A list of search result titles and URLs.
+    """
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=10):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+
+    except Exception as e:
+        print(f"Error during DuckDuckGo search: {e}")
+        return []
+
+def main():
+    # Create agents
+    researcher = Agent(
+        name="Researcher",
+        role="Senior Research Analyst",
+        goal="Uncover cutting-edge developments in AI and data science",
+        backstory="""You are an expert at a technology research group, 
+        skilled in identifying trends and analyzing complex data.""",
+        verbose=True,
+        allow_delegation=False,
+        tools=[internet_search_tool],
+        llm="gpt-4o",
+        markdown=True,
+        reflect_llm="gpt-4o",
+        min_reflect=2,
+        max_reflect=4
+    )
+    
+    writer = Agent(
+        name="Writer",
+        role="Tech Content Strategist",
+        goal="Craft compelling content on tech advancements",
+        backstory="""You are a content strategist known for 
+        making complex tech topics interesting and easy to understand.""",
+        verbose=True,
+        allow_delegation=True,
+        llm="gpt-4o",
+        tools=[],
+        markdown=True
+    )
+
+    # Create tasks with callbacks
+    task1 = Task(
+        name="research_task",
+        description="""Analyze 2024's AI advancements. 
+        Find major trends, new technologies, and their effects.""",
+        expected_output="""A detailed report on 2024 AI advancements""",
+        agent=researcher,
+        tools=[internet_search_tool],
+        callback=task_callback
+    )
+
+    task2 = Task(
+        name="writing_task",
+        description="""Create a blog post about major AI advancements using the insights you have.
+        Make it interesting, clear, and suited for tech enthusiasts. 
+        It should be at least 4 paragraphs long.""",
+        expected_output="A blog post of at least 4 paragraphs",
+        agent=writer,
+        context=[task1],
+        callback=task_callback,
+        tools=[]
+    )
+
+    task3 = Task(
+        name="json_task",
+        description="""Create a json object with a title of "My Task" and content of "My content".""",
+        expected_output="""JSON output with title and content""",
+        agent=researcher,
+        callback=task_callback
+    )
+
+    task4 = Task(
+        name="save_output_task",
+        description="""Save the AI blog post to a file""",
+        expected_output="""File saved successfully""",
+        agent=writer,
+        context=[task2],
+        output_file='test.txt',
+        create_directory=True,
+        callback=task_callback
+    )
+
+    # Create and run agents manager
+    agents = PraisonAIAgents(
+        agents=[researcher, writer],
+        tasks=[task1, task2, task3, task4],
+        verbose=True,
+        process="sequential",
+        manager_llm="gpt-4o"
+    )
+
+    agents.start()
+
+if __name__ == "__main__":
+    main()

agents/example_custom_tools.py

@@ -0,0 +1,151 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from typing import List, Dict, Union
+from duckduckgo_search import DDGS
+from langchain_community.tools import YouTubeSearchTool
+from langchain_community.utilities import WikipediaAPIWrapper
+
+# 1. Tool
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform a search using DuckDuckGo.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        list: A list of search result titles, URLs, and snippets.
+    """
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=10):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+
+    except Exception as e:
+        print(f"Error during DuckDuckGo search: {e}")
+        return []
+    
+def youtube_search_tool(query: str, inspect: bool = False, max_results: int = 2):
+    """
+    Provide a custom wrapper around the YouTubeSearchTool.
+    
+    Args:
+        query (str): The search query for YouTube.
+        inspect (bool): If True, returns tool inspection info instead of search results.
+        max_results (int): Maximum number of results to return (default: 2).
+    Returns:
+        Union[List[str], dict]: List of YouTube video URLs or tool inspection info.
+    """
+    yt = YouTubeSearchTool()
+    
+    if inspect:
+        inspection_info = {
+            "type": type(yt),
+            "attributes": [attr for attr in dir(yt) if not attr.startswith('_')],
+            "methods": {
+                "run": getattr(yt, 'run', None),
+                "arun": getattr(yt, 'arun', None)
+            },
+            "properties": {
+                "name": getattr(yt, 'name', 'youtube_search'),
+                "description": getattr(yt, 'description', 'Search YouTube videos'),
+                "return_direct": getattr(yt, 'return_direct', False)
+            }
+        }
+        return inspection_info
+    
+    # Format query with max_results
+    formatted_query = f"{query}, {max_results}"
+    return yt.run(formatted_query)
+
+def wikipedia_search_tool(query: str, inspect: bool = False, max_chars: int = 4000, top_k: int = 3):
+    """
+    Provide a custom wrapper around langchain_community's WikipediaAPIWrapper.
+
+    Args:
+        query (str): A search query for Wikipedia.
+        inspect (bool): If True, returns tool inspection info instead of search results.
+        max_chars (int): Maximum characters to return (default: 4000).
+        top_k (int): Number of top results to consider (default: 3).
+    Returns:
+        Union[str, dict]: Summary from Wikipedia or tool inspection info if inspect=True.
+    """
+    w = WikipediaAPIWrapper(
+        top_k_results=top_k,
+        doc_content_chars_max=max_chars,
+        lang='en'
+    )
+    
+    if inspect:
+        inspection_info = {
+            "type": type(w),
+            "attributes": [attr for attr in dir(w) if not attr.startswith('_')],
+            "methods": {
+                "run": getattr(w, 'run', None),
+                "arun": getattr(w, 'arun', None)
+            },
+            "properties": {
+                "name": "wikipedia",
+                "description": "Search and get summaries from Wikipedia",
+                "top_k": w.top_k_results,
+                "lang": w.lang,
+                "max_chars": w.doc_content_chars_max
+            }
+        }
+        return inspection_info
+    
+    try:
+        result = w.run(query)
+        return result
+    except Exception as e:
+        return f"Error searching Wikipedia: {str(e)}"
+
+# 2. Agent
+data_agent = Agent(
+    name="DataCollector",
+    role="Search Specialist",
+    goal="Perform internet searches to collect relevant information.",
+    backstory="Expert in finding and organizing internet data from multiple sources.",
+    tools=[internet_search_tool, youtube_search_tool, wikipedia_search_tool],
+    self_reflect=False
+)
+
+# 3. Tasks
+collect_task = Task(
+    description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
+    expected_output="List of search results with titles, URLs, and snippets.",
+    agent=data_agent,
+    name="collect_data",
+    is_start=True,
+    next_tasks=["validate_data"]
+)
+
+validate_task = Task(
+    description="""Validate the collected data. Check if:
+    1. At least 5 results are returned.
+    2. Each result contains a title and a URL.
+    Return validation_result as 'valid' or 'invalid' only no other text.""",
+    expected_output="Validation result indicating if data is valid or invalid.",
+    agent=data_agent,
+    name="validate_data",
+    task_type="decision",
+    condition={
+        "valid": [],  # End the workflow on valid data
+        "invalid": ["collect_data"]  # Retry data collection on invalid data
+    },
+)
+
+# 4. Workflow
+agents = PraisonAIAgents(
+    agents=[data_agent],
+    tasks=[collect_task, validate_task],
+    verbose=1,
+    process="workflow"
+)
+
+agents.start()

agents/example_sequential.py

@@ -0,0 +1,141 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents, error_logs
+from duckduckgo_search import DDGS
+
+def my_callback(output):
+    print(f"Callback Task output: {output}")
+
+def internet_search_tool(query) -> list:
+    """
+    Perform a search using DuckDuckGo.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        list: A list of search result titles and URLs.
+    """
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=10):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+
+    except Exception as e:
+        print(f"Error during DuckDuckGo search: {e}")
+        return []
+
+# Create agents
+researcher = Agent(
+    name="Researcher",
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI and data science",
+    backstory="""You are an expert at a technology research group, 
+    skilled in identifying trends and analyzing complex data.""",
+    verbose=True,
+    allow_delegation=False,
+    tools=[internet_search_tool],
+    llm="gpt-4o",
+    markdown=True,
+    reflect_llm="gpt-4o",
+    min_reflect=2,
+    max_reflect=4
+)
+writer = Agent(
+    name="Writer",
+    role="Tech Content Strategist",
+    goal="Craft compelling content on tech advancements",
+    backstory="""You are a content strategist known for 
+    making complex tech topics interesting and easy to understand.""",
+    verbose=True,
+    allow_delegation=True,
+    llm="gpt-4o",
+    tools=[],
+    markdown=True
+)
+
+# Create tasks
+task1 = Task(
+    name="research_task",
+    description="""Analyze 2024's AI advancements. 
+    Find major trends, new technologies, and their effects.""",
+    expected_output="""A detailed report on 2024 AI advancements""",
+    agent=researcher,
+    tools=[internet_search_tool]
+)
+
+task2 = Task(
+    name="writing_task",
+    description="""Create a blog post about major AI advancements using the insights you have.
+    Make it interesting, clear, and suited for tech enthusiasts. 
+    It should be at least 4 paragraphs long. 
+    Also, call the get_weather tool to get the weather in Paris.""",
+    expected_output="A blog post of at least 4 paragraphs, and weather in Paris",
+    agent=writer,
+    context=[task1],
+    callback=my_callback,
+    tools=[]
+)
+
+task3 = Task(
+    name="json_task",
+    description="""Create a json object with a title of "My Task" and content of "My content".""",
+    expected_output="""JSON output with title and content""",
+    agent=researcher,
+)
+
+task4 = Task(
+    name="save_output_task",
+    description="""Save the AI blog post to a file""",
+    expected_output="""File saved successfully""",
+    agent=writer,
+    context=[task2],
+    output_file='outputs/ai_blog_post.txt',
+    create_directory=True
+)
+
+# Create and run agents manager
+agents = PraisonAIAgents(
+    agents=[researcher, writer],
+    tasks=[task1, task2, task3, task4],
+    verbose=False,
+    process="sequential",  # "sequential" or "hierarchical"
+    manager_llm="gpt-4o"
+)
+
+result = agents.start()
+
+# Print results and error summary
+print("\n=== Task Results ===")
+for task_id, task_status in result['task_status'].items():
+    print(f"Task {task_id}: {task_status}")
+    if task_result := result['task_results'].get(task_id):
+        print(f"Output: {task_result.raw[:200]}...")  # Show first 200 chars
+
+# Print task details
+print("\n=== Task Details ===")
+for i in range(4):
+    print(agents.get_task_details(i))
+
+# Print agent details
+print("\n=== Agent Details ===")
+print(agents.get_agent_details('Researcher'))
+print(agents.get_agent_details('Writer'))
+
+# Print any errors
+if error_logs:
+    print("\n=== Error Summary ===")
+    for err in error_logs:
+        print(f"- {err}")
+        if "parsing self-reflection json" in err:
+            print("  Reason: The self-reflection JSON response was not valid JSON.")
+        elif "Error: Task with ID" in err:
+            print("  Reason: Task ID referenced does not exist.")
+        elif "saving task output to file" in err:
+            print("  Reason: Possible file permissions or invalid path.")
+        else:
+            print("  Reason not identified")

agents/langchain_example.py

@@ -0,0 +1,30 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from langchain_community.tools import YouTubeSearchTool
+from langchain_community.utilities import WikipediaAPIWrapper
+
+# Create an agent with both tools
+agent = Agent(
+    name="SearchAgent",
+    role="Research Assistant",
+    goal="Search for information from multiple sources",
+    backstory="I am an AI assistant that can search YouTube and Wikipedia.",
+    tools=[YouTubeSearchTool, WikipediaAPIWrapper],
+    self_reflect=False
+)
+
+# Create tasks to demonstrate both tools
+task = Task(
+    name="search_task",
+    description="Search for information about 'AI advancements' on both YouTube and Wikipedia",
+    expected_output="Combined information from YouTube videos and Wikipedia articles",
+    agent=agent
+)
+
+# Create and start the workflow
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    verbose=True
+)
+
+agents.start()

agents/langchain_logs.py

@@ -0,0 +1,73 @@
+from langchain_community.tools import YouTubeSearchTool
+from langchain_community.utilities import WikipediaAPIWrapper
+
+def test_tools():
+    """Test both YouTube and Wikipedia tools"""
+    
+    print("\n=== Testing YouTube Search ===")
+    # Test YouTube
+    yt = YouTubeSearchTool()
+    
+    # Test basic search
+    print("\nBasic YouTube Search:")
+    query = "AI advancements 2024"
+    print(f"Query: {query}")
+    result = yt.run(query)
+    print(f"Results: {result}")
+    
+    # Test with max results
+    print("\nYouTube Search with max results:")
+    query = "AI advancements 2024, 3"  # Format: query, max_results
+    print(f"Query: {query}")
+    result = yt.run(query)
+    print(f"Results: {result}")
+    
+    print("\n=== Testing Wikipedia Search ===")
+    # Test Wikipedia
+    wiki = WikipediaAPIWrapper(
+        top_k_results=2,
+        doc_content_chars_max=500
+    )
+    
+    # Test basic search
+    print("\nBasic Wikipedia Search:")
+    query = "Artificial Intelligence"
+    print(f"Query: {query}")
+    result = wiki.run(query)
+    print(f"Results: {result[:200]}..." if result else "No result")
+    
+    # Test with different parameters
+    print("\nWikipedia Search with different language:")
+    wiki_fr = WikipediaAPIWrapper(
+        lang='fr',
+        top_k_results=1,
+        doc_content_chars_max=300
+    )
+    result = wiki_fr.run("Intelligence Artificielle")
+    print(f"French Results: {result[:200]}..." if result else "No result")
+
+def print_tool_info():
+    """Print detailed information about the tools"""
+    
+    print("\n=== Tool Information ===")
+    
+    # YouTube Tool Info
+    print("\nYouTube Tool:")
+    yt = YouTubeSearchTool()
+    print("Type:", type(yt))
+    print("Name:", getattr(yt, 'name', None))
+    print("Description:", getattr(yt, 'description', None))
+    print("Arguments:", getattr(yt, 'args', None))
+    
+    # Wikipedia Tool Info
+    print("\nWikipedia Tool:")
+    wiki = WikipediaAPIWrapper()
+    print("Type:", type(wiki))
+    print("Available Settings:")
+    print("- Language:", wiki.lang)
+    print("- Top K Results:", wiki.top_k_results)
+    print("- Max Characters:", wiki.doc_content_chars_max)
+
+if __name__ == "__main__":
+    print_tool_info()
+    test_tools() 

agents/memory_example.py

@@ -0,0 +1,183 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import logging
+import os
+
+def main():
+    # Initialize memory config
+    memory_config = {
+        "provider": "rag",
+        "use_embedding": True,
+        "storage": {
+            "type": "sqlite",
+            "path": "./.praison/memory.db"
+        },
+        "rag_db_path": "./.praison/chroma_db"
+    }
+    
+    # Test facts
+    fact1 = "The capital city of Jujuha is Hahanu and its population is 102300"
+    fact2 = "Three main ingredients in a classic proloder are eggs, sugar, and flour"
+    fact3 = "The year the first Josinga was released is 2007"
+    
+    # # Check if database exists
+    # if os.path.exists("./memory.db"):
+    #     logger.info("Found existing memory database")
+    # else:
+    #     logger.info("Creating new memory database")
+    
+    # Create task config (without memory config since it's moved to PraisonAIAgents)
+    task_config = {}
+
+    # Create agents with different roles
+    researcher = Agent(
+        role="Research Analyst",
+        goal="Research and document key information about topics",
+        backstory="Expert at analyzing and storing information in memory",
+        llm="gpt-4o-mini"
+    )
+    
+    retriever = Agent(
+        role="Information Retriever",
+        goal="Retrieve and verify stored information from memory",
+        backstory="Specialist in searching and validating information from memory",
+        llm="gpt-4o-mini"
+    )
+
+    # Task 1: Process the facts
+    store_task = Task(
+        description=f"""
+        Process and analyze this information:
+        1. {fact1}
+        2. {fact2}
+        3. {fact3}
+
+        Provide a clear summary of each fact.
+        """,
+        expected_output="""
+        Clear statements summarizing each fact.
+        Example format:
+        1. [Summary of fact 1]
+        2. [Summary of fact 2]
+        3. [Summary of fact 3]
+        """,
+        agent=researcher
+    )
+
+    # Task 2: Write essay about AI
+    verify_task = Task(
+        description="""
+        write few points about AI
+        """,
+        expected_output="Points about AI",
+        agent=retriever
+    )
+
+    # Task 3: Query memory
+    query_task = Task(
+        description="""
+        Using ONLY information found in memory:
+        1. What is stored in memory about Hahanu?
+        2. What ingredients for proloder are recorded in memory?
+        3. What year is stored in memory for the Josinga release?
+
+        For each answer, cite the memory record you found.
+        """,
+        expected_output="Answers based solely on memory records with citations",
+        agent=retriever
+    )
+
+    # Task 4: Query both short-term and long-term memory
+    query_both_task = Task(
+        description="""
+        Using ONLY information found in memory:
+        1. What is stored in both short-term and long-term memory about Jujuha?
+        2. What ingredients for proloder are recorded in both short-term and long-term memory?
+        3. What year is stored in both short-term and long-term memory for the Josinga release?
+
+        For each answer, cite the memory record you found.
+        """,
+        expected_output="Answers based solely on memory records with citations",
+        agent=retriever
+    )
+
+    # Initialize PraisonAIAgents with memory configuration
+    agents = PraisonAIAgents(
+        agents=[researcher, retriever],
+        tasks=[store_task, verify_task, query_task, query_both_task],
+        verbose=True,  # Use same verbose level as memory
+        memory=True,
+        embedder={
+            "provider": "openai",
+            "config": {
+                "model": "text-embedding-3-small"
+            }
+        }
+    )
+
+    # agents = PraisonAIAgents(
+    #     agents=[researcher, retriever],
+    #     tasks=[store_task, verify_task, query_task, query_both_task],
+    #     verbose=True,  # Use same verbose level as memory
+    #     memory=True
+    # )
+    
+    # Execute tasks
+    print("\nExecuting Memory Test Tasks...")
+    print("-" * 50)
+    agents.start()
+    
+    # Use shared memory for final checks
+    memory = agents.shared_memory
+    
+    # Test memory retrieval with different quality thresholds
+    if memory:
+        print("\nFinal Memory Check:")
+        print("-" * 50)
+        
+        queries = ["Jujuha", "proloder", "Josinga"]
+        for query in queries:
+            print(f"\nSearching memory for: {query}")
+            
+            # Search in both short-term and long-term memory
+            print("\nShort-term memory results:")
+            stm_results = memory.search_short_term(query)
+            if stm_results:
+                for item in stm_results:
+                    print(f"Content: {item.get('content', '')[:200]}")
+                    if 'meta' in item:
+                        print(f"Metadata: {item['meta']}")
+                    print("-" * 20)
+            else:
+                print("No results found in short-term memory")
+
+            print("\nLong-term memory results:")
+            ltm_results = memory.search_long_term(query)
+            if ltm_results:
+                for item in ltm_results:
+                    print(f"Content: {item.get('text', '')[:200]}")
+                    if 'metadata' in item:
+                        print(f"Metadata: {item['metadata']}")
+                    print("-" * 20)
+            else:
+                print("No results found in long-term memory")
+                
+            # Also check ChromaDB if using RAG
+            if memory.use_rag and hasattr(memory, "chroma_col"):
+                print("\nChromaDB results:")
+                try:
+                    all_items = memory.chroma_col.get()
+                    print(f"Found {len(all_items['ids'])} items in ChromaDB")
+                    for i in range(len(all_items['ids'])):
+                        print(f"ID: {all_items['ids'][i]}")
+                        print(f"Content: {all_items['documents'][i][:200]}")
+                        print(f"Metadata: {all_items['metadatas'][i]}")
+                        print("-" * 20)
+                except Exception as e:
+                    print(f"Error querying ChromaDB: {e}")
+            
+            print("-" * 30)
+    else:
+        print("\nNo memory available for final checks")
+
+if __name__ == "__main__":
+    main()

agents/memory_simple.py

@@ -0,0 +1,35 @@
+from praisonaiagents.agents.agents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import duckduckgo
+
+research_agent = Agent(
+    role="Research Analyst",
+    goal="Research and document key information about topics",
+    backstory="Expert at analyzing and storing information in memory",
+    llm="gpt-4o-mini",
+    tools=[duckduckgo]
+)
+
+blog_agent = Agent(
+    role="Blog Writer",
+    goal="Write a blog post about the research",
+    backstory="Expert at writing blog posts",
+    llm="gpt-4o-mini"
+)
+
+research_task = Task(
+    description="Research and document key information about topics",
+    agent=research_agent
+)
+
+blog_task = Task(
+    description="Write a blog post about the research",
+    agent=blog_agent
+)
+
+agents = PraisonAIAgents(
+    agents=[research_agent, blog_agent],
+    tasks=[research_task, blog_task],
+    memory=True
+)   
+
+agents.start()

agents/mini_agents_example.py

@@ -0,0 +1,6 @@
+from praisonaiagents import Agent, Agents, Tools
+
+research_agent = Agent(instructions="You are a research agent to search internet about AI 2024", tools=[Tools.internet_search])
+summarise_agent = Agent(instructions="You are a summarize agent to summarise in points")
+agents = Agents(agents=[research_agent, summarise_agent])
+agents.start()

agents/multimodal.py

@@ -0,0 +1,59 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Vision Analysis Agent
+vision_agent = Agent(
+    name="VisionAnalyst",
+    role="Computer Vision Specialist",
+    goal="Analyze images and videos to extract meaningful information",
+    backstory="""You are an expert in computer vision and image analysis.
+    You excel at describing images, detecting objects, and understanding visual content.""",
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# 1. Task with Image URL
+task1 = Task(
+    name="analyze_landmark",
+    description="Describe this famous landmark and its architectural features.",
+    expected_output="Detailed description of the landmark's architecture and significance",
+    agent=vision_agent,
+    images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
+)
+
+# 2. Task with Local Image File
+task2 = Task(
+    name="analyze_local_image",
+    description="What objects can you see in this image? Describe their arrangement.",
+    expected_output="Detailed description of objects and their spatial relationships",
+    agent=vision_agent,
+    images=["image.jpg"] 
+)
+
+# 3. Task with Video File
+task3 = Task(
+    name="analyze_video",
+    description="""Watch this video and provide:
+    1. A summary of the main events
+    2. Key objects and people visible
+    3. Any text or important information shown
+    4. The overall context and setting""",
+    expected_output="Comprehensive analysis of the video content",
+    agent=vision_agent,
+    images=["video.mp4"]  
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[vision_agent],
+    tasks=[task1, task2, task3],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+result = agents.start()
+
+# Print results
+for task_id, task_result in result["task_results"].items():
+    print(f"\nTask {task_id} Result:")
+    print(task_result.raw)

agents/praisonaiagents/__init__.py

@@ -5,6 +5,9 @@ Praison AI Agents - A package for hierarchical AI agent task execution
 from .agent.agent import Agent
 from .agents.agents import PraisonAIAgents
 from .task.task import Task
+from .tools.tools import Tools
+from .agents.autoagents import AutoAgents
+from .memory.memory import Memory
 from .main import (
     TaskOutput,
     ReflectionOutput,
@@ -16,14 +19,24 @@ from .main import (
     display_generating,
     clean_triple_backticks,
     error_logs,
+    register_display_callback,
+    sync_display_callbacks,
+    async_display_callbacks,
 )
 
+# Add Agents as an alias for PraisonAIAgents
+Agents = PraisonAIAgents
+
 __all__ = [
     'Agent',
     'PraisonAIAgents',
+    'Agents',
+    'Tools',
     'Task',
     'TaskOutput',
     'ReflectionOutput',
+    'AutoAgents',
+    'Memory',
     'display_interaction',
     'display_self_reflection',
     'display_instruction',
@@ -32,4 +45,7 @@ __all__ = [
     'display_generating',
     'clean_triple_backticks',
     'error_logs',
+    'register_display_callback',
+    'sync_display_callbacks',
+    'async_display_callbacks',
 ] 

agents/praisonaiagents/agent/agent.py

@@ -1,9 +1,12 @@
-import logging
-import json
+import os
 import time
-from typing import List, Optional, Any, Dict, Union, Literal
+import json
+import logging
+import asyncio
+from typing import List, Optional, Any, Dict, Union, Literal, TYPE_CHECKING
 from rich.console import Console
 from rich.live import Live
+from openai import AsyncOpenAI
 from ..main import (
     display_error,
     display_tool_call,
@@ -15,22 +18,152 @@ from ..main import (
     client,
     error_logs
 )
+import inspect
+
+if TYPE_CHECKING:
+    from ..task.task import Task
 
 class Agent:
+    def _generate_tool_definition(self, function_name):
+        """
+        Generate a tool definition from a function name by inspecting the function.
+        """
+        logging.debug(f"Attempting to generate tool definition for: {function_name}")
+        
+        # First try to get the tool definition if it exists
+        tool_def_name = f"{function_name}_definition"
+        tool_def = globals().get(tool_def_name)
+        logging.debug(f"Looking for {tool_def_name} in globals: {tool_def is not None}")
+        
+        if not tool_def:
+            import __main__
+            tool_def = getattr(__main__, tool_def_name, None)
+            logging.debug(f"Looking for {tool_def_name} in __main__: {tool_def is not None}")
+        
+        if tool_def:
+            logging.debug(f"Found tool definition: {tool_def}")
+            return tool_def
+
+        # Try to find the function in the agent's tools list first
+        func = None
+        for tool in self.tools:
+            if callable(tool) and getattr(tool, '__name__', '') == function_name:
+                func = tool
+                break
+        
+        logging.debug(f"Looking for {function_name} in agent tools: {func is not None}")
+        
+        # If not found in tools, try globals and main
+        if not func:
+            func = globals().get(function_name)
+            logging.debug(f"Looking for {function_name} in globals: {func is not None}")
+            
+            if not func:
+                import __main__
+                func = getattr(__main__, function_name, None)
+                logging.debug(f"Looking for {function_name} in __main__: {func is not None}")
+
+        if not func or not callable(func):
+            logging.debug(f"Function {function_name} not found or not callable")
+            return None
+
+        import inspect
+        # Langchain tools
+        if inspect.isclass(func) and hasattr(func, 'run'):
+            original_func = func
+            func = func.run
+            function_name = original_func.__name__
+
+        sig = inspect.signature(func)
+        logging.debug(f"Function signature: {sig}")
+        
+        # Skip self, *args, **kwargs, so they don't get passed in arguments
+        parameters_list = []
+        for name, param in sig.parameters.items():
+            if name == "self":
+                continue
+            if param.kind in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD):
+                continue
+            parameters_list.append((name, param))
+
+        parameters = {
+            "type": "object",
+            "properties": {},
+            "required": []
+        }
+        
+        # Parse docstring for parameter descriptions
+        docstring = inspect.getdoc(func)
+        logging.debug(f"Function docstring: {docstring}")
+        
+        param_descriptions = {}
+        if docstring:
+            import re
+            param_section = re.split(r'\s*Args:\s*', docstring)
+            logging.debug(f"Param section split: {param_section}")
+            if len(param_section) > 1:
+                param_lines = param_section[1].split('\n')
+                for line in param_lines:
+                    line = line.strip()
+                    if line and ':' in line:
+                        param_name, param_desc = line.split(':', 1)
+                        param_descriptions[param_name.strip()] = param_desc.strip()
+        
+        logging.debug(f"Parameter descriptions: {param_descriptions}")
+
+        for name, param in parameters_list:
+            param_type = "string"  # Default type
+            if param.annotation != inspect.Parameter.empty:
+                if param.annotation == int:
+                    param_type = "integer"
+                elif param.annotation == float:
+                    param_type = "number"
+                elif param.annotation == bool:
+                    param_type = "boolean"
+                elif param.annotation == list:
+                    param_type = "array"
+                elif param.annotation == dict:
+                    param_type = "object"
+            
+            param_info = {"type": param_type}
+            if name in param_descriptions:
+                param_info["description"] = param_descriptions[name]
+            
+            parameters["properties"][name] = param_info
+            if param.default == inspect.Parameter.empty:
+                parameters["required"].append(name)
+        
+        logging.debug(f"Generated parameters: {parameters}")
+
+        # Extract description from docstring
+        description = docstring.split('\n')[0] if docstring else f"Function {function_name}"
+        
+        tool_def = {
+            "type": "function",
+            "function": {
+                "name": function_name,
+                "description": description,
+                "parameters": parameters
+            }
+        }
+        logging.debug(f"Generated tool definition: {tool_def}")
+        return tool_def
+
     def __init__(
         self,
-        name: str,
-        role: str,
-        goal: str,
-        backstory: str,
-        llm: Optional[Union[str, Any]] = "gpt-4o-mini",
+        name: Optional[str] = None,
+        role: Optional[str] = None,
+        goal: Optional[str] = None,
+        backstory: Optional[str] = None,
+        instructions: Optional[str] = None,
+        llm: Optional[Union[str, Any]] = None,
         tools: Optional[List[Any]] = None,
         function_calling_llm: Optional[Any] = None,
         max_iter: int = 20,
         max_rpm: Optional[int] = None,
         max_execution_time: Optional[int] = None,
-        memory: bool = True,
-        verbose: bool = False,
+        memory: Optional[Any] = None,
+        verbose: bool = True,
         allow_delegation: bool = False,
         step_callback: Optional[Any] = None,
         cache: bool = True,
@@ -45,15 +178,36 @@ class Agent:
         knowledge_sources: Optional[List[Any]] = None,
         use_system_prompt: Optional[bool] = True,
         markdown: bool = True,
-        self_reflect: bool = True,
-        max_reflection_iter: int = 3
+        self_reflect: bool = False,
+        max_reflect: int = 3,
+        min_reflect: int = 1,
+        reflect_llm: Optional[str] = None
     ):
-        self.name = name
-        self.role = role
-        self.goal = goal
-        self.backstory = backstory
-        self.llm = llm
-        self.tools = tools if tools else []
+        # Handle backward compatibility for required fields
+        if all(x is None for x in [name, role, goal, backstory, instructions]):
+            raise ValueError("At least one of name, role, goal, backstory, or instructions must be provided")
+
+        # If instructions are provided, use them to set role, goal, and backstory
+        if instructions:
+            self.name = name or "Agent"
+            self.role = role or "Assistant"
+            self.goal = goal or instructions
+            self.backstory = backstory or instructions
+            # Set self_reflect to False by default for instruction-based agents
+            self.self_reflect = False if self_reflect is None else self_reflect
+        else:
+            # Use provided values or defaults
+            self.name = name or "Agent"
+            self.role = role or "Assistant"
+            self.goal = goal or "Help the user with their tasks"
+            self.backstory = backstory or "I am an AI assistant"
+            # Default to True for traditional agents if not specified
+            self.self_reflect = True if self_reflect is None else self_reflect
+        
+        self.instructions = instructions
+        # Check for model name in environment variable if not provided
+        self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        self.tools = tools if tools else []  # Store original tools
         self.function_calling_llm = function_calling_llm
         self.max_iter = max_iter
         self.max_rpm = max_rpm
@@ -75,22 +229,75 @@ class Agent:
         self.use_system_prompt = use_system_prompt
         self.chat_history = []
         self.markdown = markdown
-        self.self_reflect = self_reflect
-        self.max_reflection_iter = max_reflection_iter
+        self.max_reflect = max_reflect
+        self.min_reflect = min_reflect
+        # Use the same model selection logic for reflect_llm
+        self.reflect_llm = reflect_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        self.console = Console()  # Create a single console instance for the agent
+        
+        # Initialize system prompt
+        self.system_prompt = f"""{self.backstory}\n
+Your Role: {self.role}\n
+Your Goal: {self.goal}
+        """
+
+    def generate_task(self) -> 'Task':
+        """Generate a Task object from the agent's instructions"""
+        from ..task.task import Task
+        
+        description = self.instructions if self.instructions else f"Execute task as {self.role} with goal: {self.goal}"
+        expected_output = "Complete the assigned task successfully"
+        
+        return Task(
+            name=self.name,
+            description=description,
+            expected_output=expected_output,
+            agent=self,
+            tools=self.tools
+        )
 
     def execute_tool(self, function_name, arguments):
+        """
+        Execute a tool dynamically based on the function name and arguments.
+        """
         logging.debug(f"{self.name} executing tool {function_name} with arguments: {arguments}")
-        if function_name == "get_weather":
-            location = arguments.get("location", "Unknown Location")
-            return {"temperature": "25C", "condition": "Sunny", "location": location}
-        elif function_name == "search_tool":
-            query = arguments.get("query", "AI trends in 2024")
-            return {"results": [
-                {"title": "AI advancements in 2024", "link": "url1", "summary": "Lots of advancements"},
-                {"title": "New trends in AI", "link": "url2", "summary": "New trends being found"}
-            ]}
-        else:
-            return f"Tool '{function_name}' is not recognized"
+
+        # Try to find the function in the agent's tools list first
+        func = None
+        for tool in self.tools:
+            if (callable(tool) and getattr(tool, '__name__', '') == function_name) or \
+               (inspect.isclass(tool) and tool.__name__ == function_name):
+                func = tool
+                break
+        
+        if func is None:
+            # If not found in tools, try globals and main
+            func = globals().get(function_name)
+            if not func:
+                import __main__
+                func = getattr(__main__, function_name, None)
+
+        if func:
+            try:
+                # If it's a class with run method, instantiate and call run
+                if inspect.isclass(func) and hasattr(func, 'run'):
+                    instance = func()
+                    # Extract only the parameters that run() expects
+                    run_params = {k: v for k, v in arguments.items() 
+                                if k in inspect.signature(instance.run).parameters 
+                                and k != 'self'}
+                    return instance.run(**run_params)
+                # Otherwise treat as regular function
+                elif callable(func):
+                    return func(**arguments)
+            except Exception as e:
+                error_msg = str(e)
+                logging.error(f"Error executing tool {function_name}: {error_msg}")
+                return {"error": error_msg}
+        
+        error_msg = f"Tool '{function_name}' is not callable"
+        logging.error(error_msg)
+        return {"error": error_msg}
 
     def clear_history(self):
         self.chat_history = []
@@ -99,31 +306,29 @@ class Agent:
         return f"Agent(name='{self.name}', role='{self.role}', goal='{self.goal}')"
 
     def _chat_completion(self, messages, temperature=0.2, tools=None, stream=True):
-        console = Console()
         start_time = time.time()
         logging.debug(f"{self.name} sending messages to LLM: {messages}")
 
         formatted_tools = []
+        if tools is None:
+            tools = self.tools
         if tools:
             for tool in tools:
-                if isinstance(tool, dict):
+                if isinstance(tool, str):
+                    # Generate tool definition for string tool names
+                    tool_def = self._generate_tool_definition(tool)
+                    if tool_def:
+                        formatted_tools.append(tool_def)
+                    else:
+                        logging.warning(f"Could not generate definition for tool: {tool}")
+                elif isinstance(tool, dict):
                     formatted_tools.append(tool)
                 elif hasattr(tool, "to_openai_tool"):
                     formatted_tools.append(tool.to_openai_tool())
-                elif isinstance(tool, str):
-                    formatted_tools.append({
-                        "type": "function",
-                        "function": {
-                            "name": tool,
-                            "description": f"This is a tool called {tool}",
-                            "parameters": {
-                                "type": "object",
-                                "properties": {},
-                            },
-                        }
-                    })
+                elif callable(tool):
+                    formatted_tools.append(self._generate_tool_definition(tool.__name__))
                 else:
-                    display_error(f"Warning: Tool {tool} not recognized")
+                    logging.warning(f"Tool {tool} not recognized")
 
         try:
             initial_response = client.chat.completions.create(
@@ -170,12 +375,24 @@ class Agent:
                     stream=True
                 )
                 full_response_text = ""
-                with Live(display_generating("", start_time), refresh_per_second=4) as live:
+                
+                # Create Live display with proper configuration
+                with Live(
+                    display_generating("", start_time),
+                    console=self.console,
+                    refresh_per_second=4,
+                    transient=False,  # Changed to False to preserve output
+                    vertical_overflow="ellipsis",
+                    auto_refresh=True
+                ) as live:
                     for chunk in response_stream:
                         if chunk.choices[0].delta.content:
                             full_response_text += chunk.choices[0].delta.content
                             live.update(display_generating(full_response_text, start_time))
-
+                
+                # Clear the last generating display with a blank line
+                self.console.print()
+                
                 final_response = client.chat.completions.create(
                     model=self.llm,
                     messages=messages,
@@ -199,20 +416,41 @@ class Agent:
             display_error(f"Error in chat completion: {e}")
             return None
 
-    def chat(self, prompt, temperature=0.2, tools=None, output_json=None):
+    def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
         if self.use_system_prompt:
             system_prompt = f"""{self.backstory}\n
-            Your Role: {self.role}\n
-            Your Goal: {self.goal}
+Your Role: {self.role}\n
+Your Goal: {self.goal}
             """
+            if output_json:
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
+            elif output_pydantic:
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
         else:
             system_prompt = None
-        
+
         messages = []
         if system_prompt:
             messages.append({"role": "system", "content": system_prompt})
         messages.extend(self.chat_history)
-        messages.append({"role": "user", "content": prompt})
+
+        # Modify prompt if output_json or output_pydantic is specified
+        original_prompt = prompt
+        if output_json or output_pydantic:
+            if isinstance(prompt, str):
+                prompt += "\nReturn ONLY a valid JSON object. No other text or explanation."
+            elif isinstance(prompt, list):
+                # For multimodal prompts, append to the text content
+                for item in prompt:
+                    if item["type"] == "text":
+                        item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
+                        break
+
+        if isinstance(prompt, list):
+            # If we receive a multimodal prompt list, place it directly in the user message
+            messages.append({"role": "user", "content": prompt})
+        else:
+            messages.append({"role": "user", "content": prompt})
 
         final_response_text = None
         reflection_count = 0
@@ -221,40 +459,26 @@ class Agent:
         while True:
             try:
                 if self.verbose:
-                    display_instruction(f"Agent {self.name} is processing prompt: {prompt}")
+                    # Handle both string and list prompts for instruction display
+                    display_text = prompt
+                    if isinstance(prompt, list):
+                        # Extract text content from multimodal prompt
+                        display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
+                    
+                    if display_text and str(display_text).strip():
+                        display_instruction(f"Agent {self.name} is processing prompt: {display_text}", console=self.console)
 
-                formatted_tools = []
-                if tools:
-                    for tool in tools:
-                        if isinstance(tool, dict):
-                            formatted_tools.append(tool)
-                        elif hasattr(tool, "to_openai_tool"):
-                            formatted_tools.append(tool.to_openai_tool())
-                        elif isinstance(tool, str):
-                            formatted_tools.append({
-                                "type": "function",
-                                "function": {
-                                    "name": tool,
-                                    "description": f"This is a tool called {tool}",
-                                    "parameters": {
-                                        "type": "object",
-                                        "properties": {},
-                                    },
-                                }
-                            })
-                        else:
-                            display_error(f"Warning: Tool {tool} not recognized")
-
-                response = self._chat_completion(messages, temperature=temperature, tools=formatted_tools if formatted_tools else None)
+                response = self._chat_completion(messages, temperature=temperature, tools=tools if tools else None)
                 if not response:
                     return None
-                    
+
                 tool_calls = getattr(response.choices[0].message, 'tool_calls', None)
+                response_text = response.choices[0].message.content.strip()
 
                 if tool_calls:
                     messages.append({
                         "role": "assistant",
-                        "content": response.choices[0].message.content,
+                        "content": response_text,
                         "tool_calls": tool_calls
                     })
                     
@@ -263,13 +487,13 @@ class Agent:
                         arguments = json.loads(tool_call.function.arguments)
 
                         if self.verbose:
-                            display_tool_call(f"Agent {self.name} is calling function '{function_name}' with arguments: {arguments}")
+                            display_tool_call(f"Agent {self.name} is calling function '{function_name}' with arguments: {arguments}", console=self.console)
 
                         tool_result = self.execute_tool(function_name, arguments)
 
                         if tool_result:
                             if self.verbose:
-                                display_tool_call(f"Function '{function_name}' returned: {tool_result}")
+                                display_tool_call(f"Function '{function_name}' returned: {tool_result}", console=self.console)
                             messages.append({
                                 "role": "tool",
                                 "tool_call_id": tool_call.id,
@@ -286,29 +510,37 @@ class Agent:
                     if not response:
                         return None
                     response_text = response.choices[0].message.content.strip()
-                else:
-                    response_text = response.choices[0].message.content.strip()
+
+                # Handle output_json or output_pydantic if specified
+                if output_json or output_pydantic:
+                    # Add to chat history and return raw response
+                    self.chat_history.append({"role": "user", "content": original_prompt})
+                    self.chat_history.append({"role": "assistant", "content": response_text})
+                    if self.verbose:
+                        display_interaction(original_prompt, response_text, markdown=self.markdown, 
+                                         generation_time=time.time() - start_time, console=self.console)
+                    return response_text
 
                 if not self.self_reflect:
-                    self.chat_history.append({"role": "user", "content": prompt})
+                    self.chat_history.append({"role": "user", "content": original_prompt})
                     self.chat_history.append({"role": "assistant", "content": response_text})
                     if self.verbose:
                         logging.info(f"Agent {self.name} final response: {response_text}")
-                    display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time)
+                    display_interaction(original_prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
                     return response_text
 
                 reflection_prompt = f"""
-                Reflect on your previous response: '{response_text}'.
-                Identify any flaws, improvements, or actions.
-                Provide a "satisfactory" status ('yes' or 'no').
-                Output MUST be JSON with 'reflection' and 'satisfactory'.
+Reflect on your previous response: '{response_text}'.
+Identify any flaws, improvements, or actions.
+Provide a "satisfactory" status ('yes' or 'no').
+Output MUST be JSON with 'reflection' and 'satisfactory'.
                 """
                 logging.debug(f"{self.name} reflection attempt {reflection_count+1}, sending prompt: {reflection_prompt}")
                 messages.append({"role": "user", "content": reflection_prompt})
 
                 try:
                     reflection_response = client.beta.chat.completions.parse(
-                        model=self.llm,
+                        model=self.reflect_llm if self.reflect_llm else self.llm,
                         messages=messages,
                         temperature=temperature,
                         response_format=ReflectionOutput
@@ -317,35 +549,192 @@ class Agent:
                     reflection_output = reflection_response.choices[0].message.parsed
 
                     if self.verbose:
-                        display_self_reflection(f"Agent {self.name} self reflection: reflection='{reflection_output.reflection}' satisfactory='{reflection_output.satisfactory}'")
+                        display_self_reflection(f"Agent {self.name} self reflection (using {self.reflect_llm if self.reflect_llm else self.llm}): reflection='{reflection_output.reflection}' satisfactory='{reflection_output.satisfactory}'", console=self.console)
 
                     messages.append({"role": "assistant", "content": f"Self Reflection: {reflection_output.reflection} Satisfactory?: {reflection_output.satisfactory}"})
 
-                    if reflection_output.satisfactory == "yes":
+                    # Only consider satisfactory after minimum reflections
+                    if reflection_output.satisfactory == "yes" and reflection_count >= self.min_reflect - 1:
                         if self.verbose:
-                            display_self_reflection("Agent marked the response as satisfactory")
+                            display_self_reflection("Agent marked the response as satisfactory after meeting minimum reflections", console=self.console)
+                        self.chat_history.append({"role": "user", "content": prompt})
                         self.chat_history.append({"role": "assistant", "content": response_text})
-                        display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time)
+                        display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
                         return response_text
 
-                    logging.debug(f"{self.name} reflection not satisfactory, requesting regeneration.")
+                    # Check if we've hit max reflections
+                    if reflection_count >= self.max_reflect - 1:
+                        if self.verbose:
+                            display_self_reflection("Maximum reflection count reached, returning current response", console=self.console)
+                        self.chat_history.append({"role": "user", "content": prompt})
+                        self.chat_history.append({"role": "assistant", "content": response_text})
+                        display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
+                        return response_text
+
+                    logging.debug(f"{self.name} reflection count {reflection_count + 1}, continuing reflection process")
                     messages.append({"role": "user", "content": "Now regenerate your response using the reflection you made"})
                     response = self._chat_completion(messages, temperature=temperature, tools=None, stream=True)
                     response_text = response.choices[0].message.content.strip()
+                    reflection_count += 1
+                    continue  # Continue the loop for more reflections
+
                 except Exception as e:
-                    display_error(f"Error in parsing self-reflection json {e}. Retrying")
+                    display_error(f"Error in parsing self-reflection json {e}. Retrying", console=self.console)
                     logging.error("Reflection parsing failed.", exc_info=True)
                     messages.append({"role": "assistant", "content": f"Self Reflection failed."})
+                    reflection_count += 1
+                    continue  # Continue even after error to try again
                 
-                reflection_count += 1
-
-                self.chat_history.append({"role": "user", "content": prompt})
-                self.chat_history.append({"role": "assistant", "content": response_text})
-
-                if self.verbose:
-                    logging.info(f"Agent {self.name} final response: {response_text}")
-                display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time)
-                return response_text
             except Exception as e:
-                display_error(f"Error in chat: {e}")
-                return None 
+                display_error(f"Error in chat: {e}", console=self.console)
+                return None 
+
+    def clean_json_output(self, output: str) -> str:
+        """Clean and extract JSON from response text."""
+        cleaned = output.strip()
+        # Remove markdown code blocks if present
+        if cleaned.startswith("```json"):
+            cleaned = cleaned[len("```json"):].strip()
+        if cleaned.startswith("```"):
+            cleaned = cleaned[len("```"):].strip()
+        if cleaned.endswith("```"):
+            cleaned = cleaned[:-3].strip()
+        return cleaned 
+
+    async def achat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
+        """Async version of chat method"""
+        try:
+            # Build system prompt
+            system_prompt = self.system_prompt
+            if output_json:
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
+            elif output_pydantic:
+                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
+
+            # Build messages
+            if isinstance(prompt, str):
+                messages = [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": prompt + ("\nReturn ONLY a valid JSON object. No other text or explanation." if (output_json or output_pydantic) else "")}
+                ]
+            else:
+                # For multimodal prompts
+                messages = [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": prompt}
+                ]
+                if output_json or output_pydantic:
+                    # Add JSON instruction to text content
+                    for item in messages[-1]["content"]:
+                        if item["type"] == "text":
+                            item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
+                            break
+
+            # Format tools if provided
+            formatted_tools = []
+            if tools:
+                for tool in tools:
+                    if isinstance(tool, str):
+                        tool_def = self._generate_tool_definition(tool)
+                        if tool_def:
+                            formatted_tools.append(tool_def)
+                    elif isinstance(tool, dict):
+                        formatted_tools.append(tool)
+                    elif hasattr(tool, "to_openai_tool"):
+                        formatted_tools.append(tool.to_openai_tool())
+                    elif callable(tool):
+                        formatted_tools.append(self._generate_tool_definition(tool.__name__))
+
+            # Create async OpenAI client
+            async_client = AsyncOpenAI()
+
+            # Make the API call based on the type of request
+            if tools:
+                response = await async_client.chat.completions.create(
+                    model=self.llm,
+                    messages=messages,
+                    temperature=temperature,
+                    tools=formatted_tools
+                )
+                return await self._achat_completion(response, tools)
+            elif output_json or output_pydantic:
+                response = await async_client.chat.completions.create(
+                    model=self.llm,
+                    messages=messages,
+                    temperature=temperature,
+                    response_format={"type": "json_object"}
+                )
+                # Return the raw response
+                return response.choices[0].message.content
+            else:
+                response = await async_client.chat.completions.create(
+                    model=self.llm,
+                    messages=messages,
+                    temperature=temperature
+                )
+                return response.choices[0].message.content
+        except Exception as e:
+            display_error(f"Error in chat completion: {e}")
+            return None
+
+    async def _achat_completion(self, response, tools):
+        """Async version of _chat_completion method"""
+        try:
+            message = response.choices[0].message
+            if not hasattr(message, 'tool_calls') or not message.tool_calls:
+                return message.content
+
+            results = []
+            for tool_call in message.tool_calls:
+                try:
+                    function_name = tool_call.function.name
+                    arguments = json.loads(tool_call.function.arguments)
+                    
+                    # Find the matching tool
+                    tool = next((t for t in tools if t.__name__ == function_name), None)
+                    if not tool:
+                        display_error(f"Tool {function_name} not found")
+                        continue
+                    
+                    # Check if the tool is async
+                    if asyncio.iscoroutinefunction(tool):
+                        result = await tool(**arguments)
+                    else:
+                        # Run sync function in executor to avoid blocking
+                        loop = asyncio.get_event_loop()
+                        result = await loop.run_in_executor(None, lambda: tool(**arguments))
+                    
+                    results.append(result)
+                except Exception as e:
+                    display_error(f"Error executing tool {function_name}: {e}")
+                    results.append(None)
+
+            # If we have results, format them into a response
+            if results:
+                formatted_results = "\n".join([str(r) for r in results if r is not None])
+                if formatted_results:
+                    messages = [
+                        {"role": "system", "content": self.system_prompt},
+                        {"role": "assistant", "content": "Here are the tool results:"},
+                        {"role": "user", "content": formatted_results + "\nPlease process these results and provide a final response."}
+                    ]
+                    try:
+                        async_client = AsyncOpenAI()
+                        final_response = await async_client.chat.completions.create(
+                            model=self.llm,
+                            messages=messages,
+                            temperature=0.2
+                        )
+                        return final_response.choices[0].message.content
+                    except Exception as e:
+                        display_error(f"Error in final chat completion: {e}")
+                        return formatted_results
+                return formatted_results
+            return None
+        except Exception as e:
+            display_error(f"Error in _achat_completion: {e}")
+            return None 
+
+    def run(self):
+        """Alias for start() method"""
+        return self.start() 

agents/praisonaiagents/agents/__init__.py

@@ -1,4 +1,5 @@
 """Agents module for managing multiple AI agents"""
 from .agents import PraisonAIAgents
+from .autoagents import AutoAgents
 
-__all__ = ['PraisonAIAgents'] 
+__all__ = ['PraisonAIAgents', 'AutoAgents'] 

agents/praisonaiagents/agents/agents.py

@@ -2,7 +2,7 @@ import os
 import time
 import json
 import logging
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, List
 from pydantic import BaseModel
 from rich.text import Text
 from rich.panel import Panel
@@ -10,9 +10,44 @@ from rich.console import Console
 from ..main import display_error, TaskOutput, error_logs, client
 from ..agent.agent import Agent
 from ..task.task import Task
+from ..process.process import Process, LoopItems
+import asyncio
+
+# Set up logger
+logger = logging.getLogger(__name__)
+
+def encode_file_to_base64(file_path: str) -> str:
+    """Base64-encode a file."""
+    import base64
+    with open(file_path, "rb") as f:
+        return base64.b64encode(f.read()).decode("utf-8")
+
+def process_video(video_path: str, seconds_per_frame=2):
+    """Split video into frames (base64-encoded)."""
+    import cv2
+    import base64
+    base64_frames = []
+    video = cv2.VideoCapture(video_path)
+    total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = video.get(cv2.CAP_PROP_FPS)
+    frames_to_skip = int(fps * seconds_per_frame)
+    curr_frame = 0
+    while curr_frame < total_frames:
+        video.set(cv2.CAP_PROP_POS_FRAMES, curr_frame)
+        success, frame = video.read()
+        if not success:
+            break
+        _, buffer = cv2.imencode(".jpg", frame)
+        base64_frames.append(base64.b64encode(buffer).decode("utf-8"))
+        curr_frame += frames_to_skip
+    video.release()
+    return base64_frames
 
 class PraisonAIAgents:
-    def __init__(self, agents, tasks, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None):
+    def __init__(self, agents, tasks=None, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None, memory=False, memory_config=None, embedder=None):
+        if not agents:
+            raise ValueError("At least one agent must be provided")
+            
         self.agents = agents
         self.tasks = {}
         if max_retries < 3:
@@ -22,10 +57,111 @@ class PraisonAIAgents:
         self.verbose = verbose
         self.max_retries = max_retries
         self.process = process
-        self.manager_llm = manager_llm
+        
+        # Check for manager_llm in environment variable if not provided
+        self.manager_llm = manager_llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        
+        # Set logger level based on verbose
+        if verbose >= 5:
+            logger.setLevel(logging.INFO)
+        else:
+            logger.setLevel(logging.WARNING)
+            
+        # Also set third-party loggers to WARNING
+        logging.getLogger('chromadb').setLevel(logging.WARNING)
+        logging.getLogger('openai').setLevel(logging.WARNING)
+        logging.getLogger('httpx').setLevel(logging.WARNING)
+        logging.getLogger('httpcore').setLevel(logging.WARNING)
+
+        if self.verbose:
+            logger.info(f"Using model {self.manager_llm} for manager")
+        
+        # If no tasks provided, generate them from agents
+        if tasks is None:
+            tasks = []
+            for agent in self.agents:
+                task = agent.generate_task()
+                tasks.append(task)
+            logger.info(f"Auto-generated {len(tasks)} tasks from agents")
+        else:
+            # Validate tasks for backward compatibility
+            if not tasks:
+                raise ValueError("If tasks are provided, at least one task must be present")
+            logger.info(f"Using {len(tasks)} provided tasks")
+        
+        # Add tasks and set their status
         for task in tasks:
             self.add_task(task)
             task.status = "not started"
+            
+        # If tasks were auto-generated from agents or process is sequential, set up sequential flow
+        if len(tasks) > 1 and (process == "sequential" or all(task.next_tasks == [] for task in tasks)):
+            for i in range(len(tasks) - 1):
+                # Set up next task relationship
+                tasks[i].next_tasks = [tasks[i + 1].name]
+                # Set up context for the next task to include the current task
+                if tasks[i + 1].context is None:
+                    tasks[i + 1].context = []
+                tasks[i + 1].context.append(tasks[i])
+            logger.info("Set up sequential flow with automatic context passing")
+        
+        self._state = {}  # Add state storage at PraisonAIAgents level
+        
+        # Initialize memory system
+        self.shared_memory = None
+        if memory:
+            try:
+                from ..memory.memory import Memory
+                
+                # Get memory config from parameter or first task
+                mem_cfg = memory_config
+                if not mem_cfg:
+                    mem_cfg = next((t.config.get('memory_config') for t in tasks if hasattr(t, 'config') and t.config), None)
+                    
+                # Set default memory config if none provided
+                if not mem_cfg:
+                    mem_cfg = {
+                        "provider": "rag",
+                        "use_embedding": True,
+                        "storage": {
+                            "type": "sqlite",
+                            "path": "./.praison/memory.db"
+                        },
+                        "rag_db_path": "./.praison/chroma_db"
+                    }
+                
+                # Add embedder config if provided
+                if embedder:
+                    if isinstance(embedder, dict):
+                        mem_cfg = mem_cfg or {}
+                        mem_cfg["embedder"] = embedder
+                    else:
+                        # Handle direct embedder function
+                        mem_cfg = mem_cfg or {}
+                        mem_cfg["embedder_function"] = embedder
+
+                if mem_cfg:
+                    # Pass verbose level to Memory
+                    self.shared_memory = Memory(config=mem_cfg, verbose=verbose)
+                    if verbose >= 5:
+                        logger.info("Initialized shared memory for PraisonAIAgents")
+                    
+                    # Distribute memory to tasks
+                    for task in tasks:
+                        if not task.memory:
+                            task.memory = self.shared_memory
+                            if verbose >= 5:
+                                logger.info(f"Assigned shared memory to task {task.id}")
+                            
+            except Exception as e:
+                logger.error(f"Failed to initialize shared memory: {e}")
+            
+        # Update tasks with shared memory
+        if self.shared_memory:
+            for task in tasks:
+                if not task.memory:
+                    task.memory = self.shared_memory
+                    logger.info(f"Assigned shared memory to task {task.id}")
 
     def add_task(self, task):
         task_id = self.task_id_counter
@@ -51,20 +187,34 @@ class PraisonAIAgents:
             return True
         return len(agent_output.strip()) > 0
 
-    def execute_task(self, task_id):
+    async def aexecute_task(self, task_id):
+        """Async version of execute_task method"""
         if task_id not in self.tasks:
             display_error(f"Error: Task with ID {task_id} does not exist")
             return
         task = self.tasks[task_id]
+        
+        # Only import multimodal dependencies if task has images
+        if task.images and task.status == "not started":
+            try:
+                import cv2
+                import base64
+                from moviepy import VideoFileClip
+            except ImportError as e:
+                display_error(f"Error: Missing required dependencies for image/video processing: {e}")
+                display_error("Please install with: pip install opencv-python moviepy")
+                task.status = "failed"
+                return None
+
         if task.status == "not started":
             task.status = "in progress"
 
         executor_agent = task.agent
 
         task_prompt = f"""
-        You need to do the following task: {task.description}.
-        Expected Output: {task.expected_output}.
-        """
+You need to do the following task: {task.description}.
+Expected Output: {task.expected_output}.
+"""
         if task.context:
             context_results = ""
             for context_task in task.context:
@@ -73,15 +223,62 @@ class PraisonAIAgents:
                 else:
                     context_results += f"Previous task {context_task.name if context_task.name else context_task.description} had no result.\n"
             task_prompt += f"""
-            Here are the results of previous tasks that might be useful:\n
-            {context_results}
-            """
+Here are the results of previous tasks that might be useful:\n
+{context_results}
+"""
         task_prompt += "Please provide only the final result of your work. Do not add any conversation or extra explanation."
 
         if self.verbose >= 2:
-            logging.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
-        logging.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
-        agent_output = executor_agent.chat(task_prompt, tools=task.tools)
+            logger.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
+        logger.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
+
+        if task.images:
+            def _get_multimodal_message(text_prompt, images):
+                content = [{"type": "text", "text": text_prompt}]
+
+                for img in images:
+                    # If local file path for a valid image
+                    if os.path.exists(img):
+                        ext = os.path.splitext(img)[1].lower()
+                        # If it's a .mp4, convert to frames
+                        if ext == ".mp4":
+                            frames = process_video(img, seconds_per_frame=1)
+                            content.append({"type": "text", "text": "These are frames from the video."})
+                            for f in frames:
+                                content.append({
+                                    "type": "image_url",
+                                    "image_url": {"url": f"data:image/jpg;base64,{f}"}
+                                })
+                        else:
+                            encoded = encode_file_to_base64(img)
+                            content.append({
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": f"data:image/{ext.lstrip('.')};base64,{encoded}"
+                                }
+                            })
+                    else:
+                        # Treat as a remote URL
+                        content.append({
+                            "type": "image_url",
+                            "image_url": {"url": img}
+                        })
+                return content
+
+            agent_output = await executor_agent.achat(
+                _get_multimodal_message(task_prompt, task.images),
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )
+        else:
+            agent_output = await executor_agent.achat(
+                task_prompt,
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )
+
         if agent_output:
             task_output = TaskOutput(
                 description=task.description,
@@ -98,8 +295,8 @@ class PraisonAIAgents:
                     task_output.json_dict = parsed
                     task_output.output_format = "JSON"
                 except:
-                    logging.warning(f"Warning: Could not parse output of task {task_id} as JSON")
-                    logging.debug(f"Output that failed JSON parsing: {agent_output}")
+                    logger.warning(f"Warning: Could not parse output of task {task_id} as JSON")
+                    logger.debug(f"Output that failed JSON parsing: {agent_output}")
 
             if task.output_pydantic:
                 cleaned = self.clean_json_output(agent_output)
@@ -109,8 +306,8 @@ class PraisonAIAgents:
                     task_output.pydantic = pyd_obj
                     task_output.output_format = "Pydantic"
                 except:
-                    logging.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
-                    logging.debug(f"Output that failed Pydantic parsing: {agent_output}")
+                    logger.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
+                    logger.debug(f"Output that failed Pydantic parsing: {agent_output}")
 
             task.result = task_output
             return task_output
@@ -118,6 +315,99 @@ class PraisonAIAgents:
             task.status = "failed"
             return None
 
+    async def arun_task(self, task_id):
+        """Async version of run_task method"""
+        if task_id not in self.tasks:
+            display_error(f"Error: Task with ID {task_id} does not exist")
+            return
+        task = self.tasks[task_id]
+        if task.status == "completed":
+            logger.info(f"Task with ID {task_id} is already completed")
+            return
+
+        retries = 0
+        while task.status != "completed" and retries < self.max_retries:
+            logger.debug(f"Attempt {retries+1} for task {task_id}")
+            if task.status in ["not started", "in progress"]:
+                task_output = await self.aexecute_task(task_id)
+                if task_output and self.completion_checker(task, task_output.raw):
+                    task.status = "completed"
+                    # Run execute_callback for memory operations
+                    try:
+                        await task.execute_callback(task_output)
+                    except Exception as e:
+                        logger.error(f"Error executing memory callback for task {task_id}: {e}")
+                        logger.exception(e)
+                    
+                    # Run task callback if exists
+                    if task.callback:
+                        try:
+                            if asyncio.iscoroutinefunction(task.callback):
+                                await task.callback(task_output)
+                            else:
+                                task.callback(task_output)
+                        except Exception as e:
+                            logger.error(f"Error executing task callback for task {task_id}: {e}")
+                            logger.exception(e)
+                            
+                    self.save_output_to_file(task, task_output)
+                    if self.verbose >= 1:
+                        logger.info(f"Task {task_id} completed successfully.")
+                else:
+                    task.status = "in progress"
+                    if self.verbose >= 1:
+                        logger.info(f"Task {task_id} not completed, retrying")
+                    await asyncio.sleep(1)
+                    retries += 1
+            else:
+                if task.status == "failed":
+                    logger.info("Task is failed, resetting to in-progress for another try...")
+                    task.status = "in progress"
+                else:
+                    logger.info("Invalid Task status")
+                    break
+
+        if retries == self.max_retries and task.status != "completed":
+            logger.info(f"Task {task_id} failed after {self.max_retries} retries.")
+
+    async def arun_all_tasks(self):
+        """Async version of run_all_tasks method"""
+        process = Process(
+            tasks=self.tasks,
+            agents=self.agents,
+            manager_llm=self.manager_llm,
+            verbose=self.verbose
+        )
+        
+        if self.process == "workflow":
+            async for task_id in process.aworkflow():
+                if self.tasks[task_id].async_execution:
+                    await self.arun_task(task_id)
+                else:
+                    self.run_task(task_id)
+        elif self.process == "sequential":
+            async for task_id in process.asequential():
+                if self.tasks[task_id].async_execution:
+                    await self.arun_task(task_id)
+                else:
+                    self.run_task(task_id)
+        elif self.process == "hierarchical":
+            async for task_id in process.ahierarchical():
+                if isinstance(task_id, Task):
+                    task_id = self.add_task(task_id)
+                if self.tasks[task_id].async_execution:
+                    await self.arun_task(task_id)
+                else:
+                    self.run_task(task_id)
+
+    async def astart(self):
+        """Async version of start method"""
+        await self.arun_all_tasks()
+        return {
+            "task_status": self.get_all_tasks_status(),
+            "task_results": {task_id: self.get_task_result(task_id) for task_id in self.tasks}
+        }
+
     def save_output_to_file(self, task, task_output):
         if task.output_file:
             try:
@@ -126,165 +416,243 @@ class PraisonAIAgents:
                 with open(task.output_file, "w") as f:
                     f.write(str(task_output))
                 if self.verbose >= 1:
-                    logging.info(f"Task output saved to {task.output_file}")
+                    logger.info(f"Task output saved to {task.output_file}")
             except Exception as e:
                 display_error(f"Error saving task output to file: {e}")
 
+    def execute_task(self, task_id):
+        """Synchronous version of execute_task method"""
+        if task_id not in self.tasks:
+            display_error(f"Error: Task with ID {task_id} does not exist")
+            return
+        task = self.tasks[task_id]
+        
+        logger.info(f"Starting execution of task {task_id}")
+        logger.info(f"Task config: {task.config}")
+        
+        # Initialize memory before task execution
+        if not task.memory:
+            task.memory = task.initialize_memory()
+        
+        logger.info(f"Task memory status: {'Initialized' if task.memory else 'Not initialized'}")
+        
+        # Only import multimodal dependencies if task has images
+        if task.images and task.status == "not started":
+            try:
+                import cv2
+                import base64
+                from moviepy import VideoFileClip
+            except ImportError as e:
+                display_error(f"Error: Missing required dependencies for image/video processing: {e}")
+                display_error("Please install with: pip install opencv-python moviepy")
+                task.status = "failed"
+                return None
+
+        if task.status == "not started":
+            task.status = "in progress"
+
+        executor_agent = task.agent
+
+        task_prompt = f"""
+You need to do the following task: {task.description}.
+Expected Output: {task.expected_output}.
+"""
+        if task.context:
+            context_results = ""
+            for context_task in task.context:
+                if context_task.result:
+                    context_results += f"Result of previous task {context_task.name if context_task.name else context_task.description}: {context_task.result.raw}\n"
+                else:
+                    context_results += f"Previous task {context_task.name if context_task.name else context_task.description} had no result.\n"
+            task_prompt += f"""
+Here are the results of previous tasks that might be useful:\n
+{context_results}
+"""
+        # Add memory context if available
+        if task.memory:
+            try:
+                memory_context = task.memory.build_context_for_task(task.description)
+                if memory_context:
+                    task_prompt += f"\n\nRelevant memory context:\n{memory_context}"
+            except Exception as e:
+                logger.error(f"Error getting memory context: {e}")
+
+        task_prompt += "Please provide only the final result of your work. Do not add any conversation or extra explanation."
+
+        if self.verbose >= 2:
+            logger.info(f"Executing task {task_id}: {task.description} using {executor_agent.name}")
+        logger.debug(f"Starting execution of task {task_id} with prompt:\n{task_prompt}")
+
+        if task.images:
+            def _get_multimodal_message(text_prompt, images):
+                content = [{"type": "text", "text": text_prompt}]
+
+                for img in images:
+                    # If local file path for a valid image
+                    if os.path.exists(img):
+                        ext = os.path.splitext(img)[1].lower()
+                        # If it's a .mp4, convert to frames
+                        if ext == ".mp4":
+                            frames = process_video(img, seconds_per_frame=1)
+                            content.append({"type": "text", "text": "These are frames from the video."})
+                            for f in frames:
+                                content.append({
+                                    "type": "image_url",
+                                    "image_url": {"url": f"data:image/jpg;base64,{f}"}
+                                })
+                        else:
+                            encoded = encode_file_to_base64(img)
+                            content.append({
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": f"data:image/{ext.lstrip('.')};base64,{encoded}"
+                                }
+                            })
+                    else:
+                        # Treat as a remote URL
+                        content.append({
+                            "type": "image_url",
+                            "image_url": {"url": img}
+                        })
+                return content
+
+            agent_output = executor_agent.chat(
+                _get_multimodal_message(task_prompt, task.images),
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )
+        else:
+            agent_output = executor_agent.chat(
+                task_prompt,
+                tools=task.tools,
+                output_json=task.output_json,
+                output_pydantic=task.output_pydantic
+            )
+
+        if agent_output:
+            # Store the response in memory
+            if task.memory:
+                try:
+                    task.store_in_memory(
+                        content=agent_output,
+                        agent_name=executor_agent.name,
+                        task_id=task_id
+                    )
+                except Exception as e:
+                    logger.error(f"Failed to store agent output in memory: {e}")
+
+            task_output = TaskOutput(
+                description=task.description,
+                summary=task.description[:10],
+                raw=agent_output,
+                agent=executor_agent.name,
+                output_format="RAW"
+            )
+
+            if task.output_json:
+                cleaned = self.clean_json_output(agent_output)
+                try:
+                    parsed = json.loads(cleaned)
+                    task_output.json_dict = parsed
+                    task_output.output_format = "JSON"
+                except:
+                    logger.warning(f"Warning: Could not parse output of task {task_id} as JSON")
+                    logger.debug(f"Output that failed JSON parsing: {agent_output}")
+
+            if task.output_pydantic:
+                cleaned = self.clean_json_output(agent_output)
+                try:
+                    parsed = json.loads(cleaned)
+                    pyd_obj = task.output_pydantic(**parsed)
+                    task_output.pydantic = pyd_obj
+                    task_output.output_format = "Pydantic"
+                except:
+                    logger.warning(f"Warning: Could not parse output of task {task_id} as Pydantic Model")
+                    logger.debug(f"Output that failed Pydantic parsing: {agent_output}")
+
+            task.result = task_output
+            return task_output
+        else:
+            task.status = "failed"
+            return None
+
     def run_task(self, task_id):
+        """Synchronous version of run_task method"""
         if task_id not in self.tasks:
             display_error(f"Error: Task with ID {task_id} does not exist")
             return
         task = self.tasks[task_id]
         if task.status == "completed":
-            logging.info(f"Task with ID {task_id} is already completed")
+            logger.info(f"Task with ID {task_id} is already completed")
             return
 
         retries = 0
         while task.status != "completed" and retries < self.max_retries:
-            logging.debug(f"Attempt {retries+1} for task {task_id}")
+            logger.debug(f"Attempt {retries+1} for task {task_id}")
             if task.status in ["not started", "in progress"]:
                 task_output = self.execute_task(task_id)
                 if task_output and self.completion_checker(task, task_output.raw):
                     task.status = "completed"
+                    # Run execute_callback for memory operations
+                    try:
+                        loop = asyncio.get_event_loop()
+                        loop.run_until_complete(task.execute_callback(task_output))
+                    except Exception as e:
+                        logger.error(f"Error executing memory callback for task {task_id}: {e}")
+                        logger.exception(e)
+                    
+                    # Run task callback if exists
                     if task.callback:
-                        task.callback(task_output)
+                        try:
+                            if asyncio.iscoroutinefunction(task.callback):
+                                loop.run_until_complete(task.callback(task_output))
+                            else:
+                                task.callback(task_output)
+                        except Exception as e:
+                            logger.error(f"Error executing task callback for task {task_id}: {e}")
+                            logger.exception(e)
+                            
                     self.save_output_to_file(task, task_output)
                     if self.verbose >= 1:
-                        logging.info(f"Task {task_id} completed successfully.")
+                        logger.info(f"Task {task_id} completed successfully.")
                 else:
                     task.status = "in progress"
                     if self.verbose >= 1:
-                        logging.info(f"Task {task_id} not completed, retrying")
+                        logger.info(f"Task {task_id} not completed, retrying")
                     time.sleep(1)
                     retries += 1
             else:
                 if task.status == "failed":
-                    logging.info("Task is failed, resetting to in-progress for another try...")
+                    logger.info("Task is failed, resetting to in-progress for another try...")
                     task.status = "in progress"
                 else:
-                    logging.info("Invalid Task status")
+                    logger.info("Invalid Task status")
                     break
 
         if retries == self.max_retries and task.status != "completed":
-            logging.info(f"Task {task_id} failed after {self.max_retries} retries.")
+            logger.info(f"Task {task_id} failed after {self.max_retries} retries.")
 
     def run_all_tasks(self):
-        if self.process == "sequential":
-            for task_id in self.tasks:
-                if self.tasks[task_id].status != "completed":
-                    self.run_task(task_id)
+        """Synchronous version of run_all_tasks method"""
+        process = Process(
+            tasks=self.tasks,
+            agents=self.agents,
+            manager_llm=self.manager_llm,
+            verbose=self.verbose
+        )
+        
+        if self.process == "workflow":
+            for task_id in process.workflow():
+                self.run_task(task_id)
+        elif self.process == "sequential":
+            for task_id in process.sequential():
+                self.run_task(task_id)
         elif self.process == "hierarchical":
-            logging.debug(f"Starting hierarchical task execution with {len(self.tasks)} tasks")
-            manager_agent = Agent(
-                name="Manager",
-                role="Project manager",
-                goal="Manage the entire flow of tasks and delegate them to the right agent",
-                backstory="Expert project manager to coordinate tasks among agents",
-                llm=self.manager_llm,
-                verbose=self.verbose,
-                markdown=True,
-                self_reflect=False
-            )
-
-            class ManagerInstructions(BaseModel):
-                task_id: int
-                agent_name: str
-                action: str
-
-            manager_task = Task(
-                name="manager_task",
-                description="Decide the order of tasks and which agent executes them",
-                expected_output="All tasks completed successfully",
-                agent=manager_agent
-            )
-            manager_task_id = self.add_task(manager_task)
-            logging.info(f"Created manager task with ID {manager_task_id}")
-
-            completed_count = 0
-            total_tasks = len(self.tasks) - 1
-            logging.info(f"Need to complete {total_tasks} tasks (excluding manager task)")
-
-            while completed_count < total_tasks:
-                tasks_summary = []
-                for tid, tk in self.tasks.items():
-                    if tk.name == "manager_task":
-                        continue
-                    task_info = {
-                        "task_id": tid,
-                        "name": tk.name,
-                        "description": tk.description,
-                        "status": tk.status if tk.status else "not started",
-                        "agent": tk.agent.name if tk.agent else "No agent"
-                    }
-                    tasks_summary.append(task_info)
-                    logging.info(f"Task {tid} status: {task_info}")
-
-                manager_prompt = f"""
-Here is the current status of all tasks except yours (manager_task):
-{tasks_summary}
-
-Provide a JSON with the structure:
-{{
-   "task_id": <int>,
-   "agent_name": "<string>",
-   "action": "<execute or stop>"
-}}
-"""
-
-                try:
-                    logging.info("Requesting manager instructions...")
-                    manager_response = client.beta.chat.completions.parse(
-                        model=self.manager_llm,
-                        messages=[
-                            {"role": "system", "content": manager_task.description},
-                            {"role": "user", "content": manager_prompt}
-                        ],
-                        temperature=0.7,
-                        response_format=ManagerInstructions
-                    )
-                    parsed_instructions = manager_response.choices[0].message.parsed
-                    logging.info(f"Manager instructions: {parsed_instructions}")
-                except Exception as e:
-                    display_error(f"Manager parse error: {e}")
-                    logging.error(f"Manager parse error: {str(e)}", exc_info=True)
-                    break
-
-                selected_task_id = parsed_instructions.task_id
-                selected_agent_name = parsed_instructions.agent_name
-                action = parsed_instructions.action
-
-                logging.info(f"Manager selected task_id={selected_task_id}, agent={selected_agent_name}, action={action}")
-
-                if action.lower() == "stop":
-                    logging.info("Manager decided to stop task execution")
-                    break
-
-                if selected_task_id not in self.tasks:
-                    error_msg = f"Manager selected invalid task id {selected_task_id}"
-                    display_error(error_msg)
-                    logging.error(error_msg)
-                    break
-
-                original_agent = self.tasks[selected_task_id].agent.name if self.tasks[selected_task_id].agent else "None"
-                for a in self.agents:
-                    if a.name == selected_agent_name:
-                        self.tasks[selected_task_id].agent = a
-                        logging.info(f"Changed agent for task {selected_task_id} from {original_agent} to {selected_agent_name}")
-                        break
-
-                if self.tasks[selected_task_id].status != "completed":
-                    logging.info(f"Starting execution of task {selected_task_id}")
-                    self.run_task(selected_task_id)
-                    logging.info(f"Finished execution of task {selected_task_id}, status: {self.tasks[selected_task_id].status}")
-
-                if self.tasks[selected_task_id].status == "completed":
-                    completed_count += 1
-                    logging.info(f"Task {selected_task_id} completed. Total completed: {completed_count}/{total_tasks}")
-
-            self.tasks[manager_task.id].status = "completed"
-            if self.verbose >= 1:
-                logging.info("All tasks completed under manager supervision.")
-            logging.info("Hierarchical task execution finished")
+            for task_id in process.hierarchical():
+                if isinstance(task_id, Task):
+                    task_id = self.add_task(task_id)
+                self.run_task(task_id)
 
     def get_task_status(self, task_id):
         if task_id in self.tasks:
@@ -315,4 +683,20 @@ Provide a JSON with the structure:
         return {
             "task_status": self.get_all_tasks_status(),
             "task_results": {task_id: self.get_task_result(task_id) for task_id in self.tasks}
-        } 
+        } 
+
+    def set_state(self, key: str, value: Any) -> None:
+        """Set a state value"""
+        self._state[key] = value
+
+    def get_state(self, key: str, default: Any = None) -> Any:
+        """Get a state value"""
+        return self._state.get(key, default)
+
+    def update_state(self, updates: Dict) -> None:
+        """Update multiple state values"""
+        self._state.update(updates)
+
+    def clear_state(self) -> None:
+        """Clear all state values"""
+        self._state.clear() 

agents/praisonaiagents/agents/autoagents.py

@@ -0,0 +1,335 @@
+"""
+AutoAgents - A class for automatically creating and managing AI agents and tasks.
+
+This class provides a simplified interface for creating and running AI agents with tasks.
+It automatically handles agent creation, task setup, and execution flow.
+"""
+
+from .agents import PraisonAIAgents
+from ..agent.agent import Agent
+from ..task.task import Task
+from typing import List, Any, Optional, Dict, Union
+import logging
+import os
+from pydantic import BaseModel, Field
+from ..main import display_instruction, display_tool_call, display_interaction, client
+
+# Define Pydantic models for structured output
+class TaskConfig(BaseModel):
+    name: str
+    description: str
+    expected_output: str
+    tools: List[str]
+
+class AgentConfig(BaseModel):
+    name: str
+    role: str
+    goal: str
+    backstory: str
+    tools: List[str]
+    tasks: List[TaskConfig]
+
+class AutoAgentsConfig(BaseModel):
+    main_instruction: str
+    process_type: str
+    agents: List[AgentConfig]
+
+class AutoAgents(PraisonAIAgents):
+    def __init__(
+        self,
+        instructions: str,
+        tools: Optional[List[Any]] = None,
+        verbose: bool = False,
+        process: str = "sequential",
+        manager_llm: Optional[str] = None,
+        max_retries: int = 5,
+        completion_checker: Optional[Any] = None,
+        allow_code_execution: bool = False,
+        memory: bool = True,
+        markdown: bool = True,
+        self_reflect: bool = False,
+        max_reflect: int = 3,
+        min_reflect: int = 1,
+        llm: Optional[str] = None,
+        function_calling_llm: Optional[str] = None,
+        respect_context_window: bool = True,
+        code_execution_mode: str = "safe",
+        embedder_config: Optional[Dict[str, Any]] = None,
+        knowledge_sources: Optional[List[Any]] = None,
+        use_system_prompt: bool = True,
+        cache: bool = True,
+        allow_delegation: bool = False,
+        step_callback: Optional[Any] = None,
+        system_template: Optional[str] = None,
+        prompt_template: Optional[str] = None,
+        response_template: Optional[str] = None,
+        max_rpm: Optional[int] = None,
+        max_execution_time: Optional[int] = None,
+        max_iter: int = 20,
+        reflect_llm: Optional[str] = None,
+        base_url: Optional[str] = None,
+        api_key: Optional[str] = None,
+        max_agents: int = 3  # New parameter for maximum number of agents
+    ):
+        """Initialize AutoAgents with configuration for automatic agent and task creation."""
+        if max_agents < 1:
+            raise ValueError("max_agents must be at least 1")
+        if max_agents > 10:
+            raise ValueError("max_agents cannot exceed 10")
+            
+        # Store all configuration parameters first
+        self.instructions = instructions
+        self.tools = tools or []
+        self.verbose = verbose  # Store verbose parameter
+        self.max_agents = max_agents  # Store max_agents parameter
+        self.allow_code_execution = allow_code_execution
+        self.memory = memory
+        self.markdown = markdown
+        self.self_reflect = self_reflect
+        self.max_reflect = max_reflect
+        self.min_reflect = min_reflect
+        self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        self.function_calling_llm = function_calling_llm
+        self.respect_context_window = respect_context_window
+        self.code_execution_mode = code_execution_mode
+        self.embedder_config = embedder_config
+        self.knowledge_sources = knowledge_sources
+        self.use_system_prompt = use_system_prompt
+        self.cache = cache
+        self.allow_delegation = allow_delegation
+        self.step_callback = step_callback
+        self.system_template = system_template
+        self.prompt_template = prompt_template
+        self.response_template = response_template
+        self.max_rpm = max_rpm
+        self.max_execution_time = max_execution_time
+        self.max_iter = max_iter
+        self.reflect_llm = reflect_llm
+        
+        # Display initial instruction
+        if self.verbose:
+            display_instruction(f"🎯 Main Task: {self.instructions}")
+            display_instruction(f"📊 Maximum Agents: {self.max_agents}")
+            if self.tools:
+                tool_names = [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools]
+                display_tool_call(f"🛠️  Available Tools: {', '.join(tool_names)}")
+        
+        # Generate agents and tasks configuration
+        config = self._generate_config()
+        agents, tasks = self._create_agents_and_tasks(config)
+        
+        # Display agents and their tasks
+        if self.verbose:
+            self._display_agents_and_tasks(agents, tasks)
+        
+        # Initialize parent class with generated agents and tasks
+        super().__init__(
+            agents=agents,
+            tasks=tasks,
+            verbose=verbose,
+            completion_checker=completion_checker,
+            max_retries=max_retries,
+            process=process,
+            manager_llm=manager_llm
+        )
+
+    def _display_agents_and_tasks(self, agents: List[Agent], tasks: List[Task]):
+        """Display the created agents and their assigned tasks"""
+        display_instruction("\n🤖 Generated Agents and Tasks:")
+        
+        # Create a mapping of agents to their tasks
+        agent_tasks = {}
+        for task in tasks:
+            if task.agent not in agent_tasks:
+                agent_tasks[task.agent] = []
+            agent_tasks[task.agent].append(task)
+        
+        # Display each agent and their tasks
+        for agent in agents:
+            agent_tools = [t.__name__ if hasattr(t, '__name__') else str(t) for t in agent.tools]
+            display_interaction(
+                f"\n👤 Agent: {agent.name}",
+                f"""Role: {agent.role}
+Goal: {agent.goal}
+Tools: {', '.join(agent_tools)}"""
+            )
+            
+            # Display tasks for this agent
+            if agent in agent_tasks:
+                for i, task in enumerate(agent_tasks[agent], 1):
+                    task_tools = [t.__name__ if hasattr(t, '__name__') else str(t) for t in task.tools]
+                    display_instruction(
+                        f"""  📋 Task {i}: {task.name}
+     Description: {task.description}
+     Expected Output: {task.expected_output}
+     Tools: {', '.join(task_tools)}"""
+                    )
+
+    def _get_available_tools(self) -> List[str]:
+        """Get list of available tools"""
+        if not self.tools:
+            return []
+        return [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools]
+
+    def _get_tool_by_name(self, tool_name: str) -> Optional[Any]:
+        """Get tool object by its name"""
+        for tool in self.tools:
+            if (hasattr(tool, '__name__') and tool.__name__ == tool_name) or str(tool) == tool_name:
+                return tool
+        return None
+
+    def _assign_tools_to_agent(self, agent_config: AgentConfig) -> List[Any]:
+        """
+        Assign appropriate tools to an agent based on its role and tasks.
+        
+        Args:
+            agent_config: The agent configuration containing role and required tools
+            
+        Returns:
+            List of tool objects assigned to this agent
+        """
+        assigned_tools = []
+        tool_names = set(agent_config.tools)
+        
+        # Also look at task requirements
+        for task in agent_config.tasks:
+            tool_names.update(task.tools)
+        
+        # Assign tools that match the requirements
+        for tool_name in tool_names:
+            tool = self._get_tool_by_name(tool_name)
+            if tool:
+                assigned_tools.append(tool)
+        
+        # If no specific tools matched but we have tools available,
+        # assign all tools to ensure functionality
+        if not assigned_tools and self.tools:
+            assigned_tools = self.tools
+            
+        return assigned_tools
+
+    def _generate_config(self) -> AutoAgentsConfig:
+        """Generate the configuration for agents and tasks"""
+        prompt = f"""
+Generate a configuration for AI agents to accomplish this task: "{self.instructions}"
+
+The configuration should include:
+1. A main instruction that clearly states the overall goal
+2. A process type (sequential, workflow, or hierarchical)
+3. A list of maximum {self.max_agents} agents (no more, no less), each with:
+   - Name, role, goal, and backstory
+   - List of required tools from: {self._get_available_tools()}
+   - Only add tools that are needed for the agent to perform its task
+   - Only one task per agent. Add more than one task if absolutely necessary.
+   - List of specific tasks they need to perform
+   - Whether they should self-reflect or allow delegation
+
+Requirements:
+1. Each agent should have clear, focused responsibilities
+2. Tasks should be broken down into manageable steps
+3. Tool selection should be appropriate for each task
+4. The process type should match the task requirements
+5. Generate maximum {self.max_agents} agents to handle this task efficiently
+
+Return the configuration in a structured JSON format matching the AutoAgentsConfig schema.
+"""
+        
+        try:
+            response = client.beta.chat.completions.parse(
+                model=self.llm,
+                response_format=AutoAgentsConfig,
+                messages=[
+                    {"role": "system", "content": "You are a helpful assistant designed to generate AI agent configurations."},
+                    {"role": "user", "content": prompt}
+                ]
+            )
+            
+            # Ensure we have exactly max_agents number of agents
+            if len(response.choices[0].message.parsed.agents) > self.max_agents:
+                response.choices[0].message.parsed.agents = response.choices[0].message.parsed.agents[:self.max_agents]
+            elif len(response.choices[0].message.parsed.agents) < self.max_agents:
+                logging.warning(f"Generated {len(response.choices[0].message.parsed.agents)} agents, expected {self.max_agents}")
+            
+            return response.choices[0].message.parsed
+        except Exception as e:
+            logging.error(f"Error generating configuration: {e}")
+            raise
+
+    def _create_agents_and_tasks(self, config: AutoAgentsConfig) -> tuple[List[Agent], List[Task]]:
+        """Create agents and tasks from configuration"""
+        agents = []
+        tasks = []
+        
+        for agent_config in config.agents:
+            # Get appropriate tools for this agent
+            agent_tools = self._assign_tools_to_agent(agent_config)
+            
+            # Create the agent with all parameters
+            agent = Agent(
+                name=agent_config.name,
+                role=agent_config.role,
+                goal=agent_config.goal,
+                backstory=agent_config.backstory,
+                tools=agent_tools,  # Use assigned tools
+                verbose=self.verbose >= 1,
+                allow_code_execution=self.allow_code_execution,
+                memory=self.memory,
+                markdown=self.markdown,
+                self_reflect=self.self_reflect,
+                max_reflect=self.max_reflect,
+                min_reflect=self.min_reflect,
+                llm=self.llm,
+                function_calling_llm=self.function_calling_llm,
+                respect_context_window=self.respect_context_window,
+                code_execution_mode=self.code_execution_mode,
+                embedder_config=self.embedder_config,
+                knowledge_sources=self.knowledge_sources,
+                use_system_prompt=self.use_system_prompt,
+                cache=self.cache,
+                allow_delegation=self.allow_delegation,
+                step_callback=self.step_callback,
+                system_template=self.system_template,
+                prompt_template=self.prompt_template,
+                response_template=self.response_template,
+                max_rpm=self.max_rpm,
+                max_execution_time=self.max_execution_time,
+                max_iter=self.max_iter,
+                reflect_llm=self.reflect_llm
+            )
+            agents.append(agent)
+            
+            # Create tasks for this agent
+            for task_config in agent_config.tasks:
+                # Get task-specific tools
+                task_tools = [self._get_tool_by_name(t) for t in task_config.tools]
+                task_tools = [t for t in task_tools if t]  # Remove None values
+                
+                # If no specific tools matched, use agent's tools
+                if not task_tools:
+                    task_tools = agent_tools
+                
+                task = Task(
+                    name=task_config.name,
+                    description=task_config.description,
+                    expected_output=task_config.expected_output,
+                    agent=agent,
+                    tools=task_tools  # Use task-specific tools
+                )
+                tasks.append(task)
+        
+        return agents, tasks
+
+    async def astart(self):
+        """
+        Async version of start() method.
+        Creates tasks based on the instructions, then starts execution.
+        Returns the task status and results dictionary.
+        """
+        return await super().astart()
+
+    def start(self):
+        """
+        Creates tasks based on the instructions, then starts execution.
+        Returns the task status and results dictionary.
+        """
+        return super().start() 

agents/praisonaiagents/main.py

@@ -12,6 +12,7 @@ from rich.text import Text
 from rich.markdown import Markdown
 from rich.logging import RichHandler
 from rich.live import Live
+import asyncio
 
 LOGLEVEL = os.environ.get('LOGLEVEL', 'INFO').upper()
 
@@ -25,43 +26,279 @@ logging.basicConfig(
 # Global list to store error logs
 error_logs = []
 
-def display_interaction(message: str, response: str, markdown: bool = True, generation_time: Optional[float] = None):
-    console = Console()
-    if generation_time is not None:
-        console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))
+# Separate registries for sync and async callbacks
+sync_display_callbacks = {}
+async_display_callbacks = {}
+
+# At the top of the file, add display_callbacks to __all__
+__all__ = [
+    'error_logs',
+    'register_display_callback',
+    'sync_display_callbacks',
+    'async_display_callbacks',
+    # ... other exports
+]
+
+def register_display_callback(display_type: str, callback_fn, is_async: bool = False):
+    """Register a synchronous or asynchronous callback function for a specific display type.
+    
+    Args:
+        display_type (str): Type of display event ('interaction', 'self_reflection', etc.)
+        callback_fn: The callback function to register
+        is_async (bool): Whether the callback is asynchronous
+    """
+    if is_async:
+        async_display_callbacks[display_type] = callback_fn
     else:
-        console.print(Text("Response Generation Complete", style="dim"))
+        sync_display_callbacks[display_type] = callback_fn
+
+async def execute_callback(display_type: str, **kwargs):
+    """Execute both sync and async callbacks for a given display type.
+    
+    Args:
+        display_type (str): Type of display event
+        **kwargs: Arguments to pass to the callback functions
+    """
+    # Execute synchronous callback if registered
+    if display_type in sync_display_callbacks:
+        callback = sync_display_callbacks[display_type]
+        loop = asyncio.get_event_loop()
+        await loop.run_in_executor(None, lambda: callback(**kwargs))
+    
+    # Execute asynchronous callback if registered
+    if display_type in async_display_callbacks:
+        callback = async_display_callbacks[display_type]
+        await callback(**kwargs)
+
+def _clean_display_content(content: str, max_length: int = 20000) -> str:
+    """Helper function to clean and truncate content for display."""
+    if not content or not str(content).strip():
+        return ""
+        
+    content = str(content)
+    # Handle base64 content
+    if "base64" in content:
+        content_parts = []
+        for line in content.split('\n'):
+            if "base64" not in line:
+                content_parts.append(line)
+        content = '\n'.join(content_parts)
+    
+    # Truncate if too long
+    if len(content) > max_length:
+        content = content[:max_length] + "..."
+    
+    return content.strip()
+
+def display_interaction(message, response, markdown=True, generation_time=None, console=None):
+    """Synchronous version of display_interaction."""
+    if console is None:
+        console = Console()
+    
+    if isinstance(message, list):
+        text_content = next((item["text"] for item in message if item["type"] == "text"), "")
+        message = text_content
+
+    message = _clean_display_content(str(message))
+    response = _clean_display_content(str(response))
+
+    # Execute synchronous callback if registered
+    if 'interaction' in sync_display_callbacks:
+        sync_display_callbacks['interaction'](
+            message=message,
+            response=response,
+            markdown=markdown,
+            generation_time=generation_time
+        )
+
+    # Rest of the display logic...
+    if generation_time:
+        console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))
 
     if markdown:
         console.print(Panel.fit(Markdown(message), title="Message", border_style="cyan"))
         console.print(Panel.fit(Markdown(response), title="Response", border_style="cyan"))
     else:
         console.print(Panel.fit(Text(message, style="bold green"), title="Message", border_style="cyan"))
-        console.print(Panel.fit(Text(response, style="bold white"), title="Response", border_style="cyan"))
+        console.print(Panel.fit(Text(response, style="bold blue"), title="Response", border_style="cyan"))
 
-def display_self_reflection(message: str):
-    console = Console()
+def display_self_reflection(message: str, console=None):
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    # Execute callback if registered
+    if 'self_reflection' in sync_display_callbacks:
+        sync_display_callbacks['self_reflection'](message=message)
+    
     console.print(Panel.fit(Text(message, style="bold yellow"), title="Self Reflection", border_style="magenta"))
 
-def display_instruction(message: str):
-    console = Console()
+def display_instruction(message: str, console=None):
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    # Execute callback if registered
+    if 'instruction' in sync_display_callbacks:
+        sync_display_callbacks['instruction'](message=message)
+    
     console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
 
-def display_tool_call(message: str):
-    console = Console()
+def display_tool_call(message: str, console=None):
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    # Execute callback if registered
+    if 'tool_call' in sync_display_callbacks:
+        sync_display_callbacks['tool_call'](message=message)
+    
     console.print(Panel.fit(Text(message, style="bold cyan"), title="Tool Call", border_style="green"))
 
-def display_error(message: str):
-    console = Console()
+def display_error(message: str, console=None):
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    # Execute callback if registered
+    if 'error' in sync_display_callbacks:
+        sync_display_callbacks['error'](message=message)
+    
     console.print(Panel.fit(Text(message, style="bold red"), title="Error", border_style="red"))
-    # Store errors
     error_logs.append(message)
 
 def display_generating(content: str = "", start_time: Optional[float] = None):
+    if not content or not str(content).strip():
+        return Panel("", title="", border_style="green")
+    
     elapsed_str = ""
     if start_time is not None:
         elapsed = time.time() - start_time
         elapsed_str = f" {elapsed:.1f}s"
+    
+    content = _clean_display_content(str(content))
+    
+    # Execute callback if registered
+    if 'generating' in sync_display_callbacks:
+        sync_display_callbacks['generating'](
+            content=content,
+            elapsed_time=elapsed_str.strip() if elapsed_str else None
+        )
+    
+    return Panel(Markdown(content), title=f"Generating...{elapsed_str}", border_style="green")
+
+# Async versions with 'a' prefix
+async def adisplay_interaction(message, response, markdown=True, generation_time=None, console=None):
+    """Async version of display_interaction."""
+    if console is None:
+        console = Console()
+    
+    if isinstance(message, list):
+        text_content = next((item["text"] for item in message if item["type"] == "text"), "")
+        message = text_content
+
+    message = _clean_display_content(str(message))
+    response = _clean_display_content(str(response))
+
+    # Execute callbacks
+    await execute_callback(
+        'interaction',
+        message=message,
+        response=response,
+        markdown=markdown,
+        generation_time=generation_time
+    )
+
+    # Rest of the display logic...
+    if generation_time:
+        console.print(Text(f"Response generated in {generation_time:.1f}s", style="dim"))
+
+    if markdown:
+        console.print(Panel.fit(Markdown(message), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Markdown(response), title="Response", border_style="cyan"))
+    else:
+        console.print(Panel.fit(Text(message, style="bold green"), title="Message", border_style="cyan"))
+        console.print(Panel.fit(Text(response, style="bold blue"), title="Response", border_style="cyan"))
+
+async def adisplay_self_reflection(message: str, console=None):
+    """Async version of display_self_reflection."""
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    if 'self_reflection' in async_display_callbacks:
+        await async_display_callbacks['self_reflection'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold yellow"), title="Self Reflection", border_style="magenta"))
+
+async def adisplay_instruction(message: str, console=None):
+    """Async version of display_instruction."""
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    if 'instruction' in async_display_callbacks:
+        await async_display_callbacks['instruction'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
+
+async def adisplay_tool_call(message: str, console=None):
+    """Async version of display_tool_call."""
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    if 'tool_call' in async_display_callbacks:
+        await async_display_callbacks['tool_call'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold cyan"), title="Tool Call", border_style="green"))
+
+async def adisplay_error(message: str, console=None):
+    """Async version of display_error."""
+    if not message or not message.strip():
+        return
+    if console is None:
+        console = Console()
+    message = _clean_display_content(str(message))
+    
+    if 'error' in async_display_callbacks:
+        await async_display_callbacks['error'](message=message)
+    
+    console.print(Panel.fit(Text(message, style="bold red"), title="Error", border_style="red"))
+    error_logs.append(message)
+
+async def adisplay_generating(content: str = "", start_time: Optional[float] = None):
+    """Async version of display_generating."""
+    if not content or not str(content).strip():
+        return Panel("", title="", border_style="green")
+    
+    elapsed_str = ""
+    if start_time is not None:
+        elapsed = time.time() - start_time
+        elapsed_str = f" {elapsed:.1f}s"
+    
+    content = _clean_display_content(str(content))
+    
+    if 'generating' in async_display_callbacks:
+        await async_display_callbacks['generating'](
+            content=content,
+            elapsed_time=elapsed_str.strip() if elapsed_str else None
+        )
+    
     return Panel(Markdown(content), title=f"Generating...{elapsed_str}", border_style="green")
 
 def clean_triple_backticks(text: str) -> str:

agents/praisonaiagents/memory/memory.py

@@ -0,0 +1,931 @@
+import os
+import sqlite3
+import json
+import time
+import shutil
+from typing import Any, Dict, List, Optional, Union, Literal
+import logging
+
+# Set up logger
+logger = logging.getLogger(__name__)
+
+try:
+    import chromadb
+    from chromadb.config import Settings as ChromaSettings
+    CHROMADB_AVAILABLE = True
+except ImportError:
+    CHROMADB_AVAILABLE = False
+    logger.warning("To use memory features, please run: pip install \"praisonaiagents[memory]\"")
+
+try:
+    import mem0
+    MEM0_AVAILABLE = True
+except ImportError:
+    MEM0_AVAILABLE = False
+
+try:
+    import openai
+    OPENAI_AVAILABLE = True
+except ImportError:
+    OPENAI_AVAILABLE = False
+
+
+
+
+class Memory:
+    """
+    A single-file memory manager covering:
+    - Short-term memory (STM) for ephemeral context
+    - Long-term memory (LTM) for persistent knowledge
+    - Entity memory (structured data about named entities)
+    - User memory (preferences/history for each user)
+    - Quality score logic for deciding which data to store in LTM
+    - Context building from multiple memory sources
+
+    Config example:
+    {
+      "provider": "rag" or "mem0" or "none",
+      "use_embedding": True,
+      "short_db": "short_term.db",
+      "long_db": "long_term.db",
+      "rag_db_path": "rag_db",   # optional path for local embedding store
+      "config": {
+        "api_key": "...",       # if mem0 usage
+        "org_id": "...",
+        "project_id": "...",
+        ...
+      }
+    }
+    """
+
+    def __init__(self, config: Dict[str, Any], verbose: int = 0):
+        self.cfg = config or {}
+        self.verbose = verbose
+        
+        # Set logger level based on verbose
+        if verbose >= 5:
+            logger.setLevel(logging.INFO)
+        else:
+            logger.setLevel(logging.WARNING)
+            
+        # Also set ChromaDB and OpenAI client loggers to WARNING
+        logging.getLogger('chromadb').setLevel(logging.WARNING)
+        logging.getLogger('openai').setLevel(logging.WARNING)
+        logging.getLogger('httpx').setLevel(logging.WARNING)
+        logging.getLogger('httpcore').setLevel(logging.WARNING)
+        logging.getLogger('chromadb.segment.impl.vector.local_persistent_hnsw').setLevel(logging.ERROR)
+            
+        self.provider = self.cfg.get("provider", "rag")
+        self.use_mem0 = (self.provider.lower() == "mem0") and MEM0_AVAILABLE
+        self.use_rag = (self.provider.lower() == "rag") and CHROMADB_AVAILABLE and self.cfg.get("use_embedding", False)
+
+        # Create .praison directory if it doesn't exist
+        os.makedirs(".praison", exist_ok=True)
+
+        # Short-term DB
+        self.short_db = self.cfg.get("short_db", ".praison/short_term.db")
+        self._init_stm()
+
+        # Long-term DB
+        self.long_db = self.cfg.get("long_db", ".praison/long_term.db")
+        self._init_ltm()
+
+        # Conditionally init Mem0 or local RAG
+        if self.use_mem0:
+            self._init_mem0()
+        elif self.use_rag:
+            self._init_chroma()
+
+    def _log_verbose(self, msg: str, level: int = logging.INFO):
+        """Only log if verbose >= 5"""
+        if self.verbose >= 5:
+            logger.log(level, msg)
+
+    # -------------------------------------------------------------------------
+    #                          Initialization
+    # -------------------------------------------------------------------------
+    def _init_stm(self):
+        """Creates or verifies short-term memory table."""
+        os.makedirs(os.path.dirname(self.short_db) or ".", exist_ok=True)
+        conn = sqlite3.connect(self.short_db)
+        c = conn.cursor()
+        c.execute("""
+        CREATE TABLE IF NOT EXISTS short_mem (
+            id TEXT PRIMARY KEY,
+            content TEXT,
+            meta TEXT,
+            created_at REAL
+        )
+        """)
+        conn.commit()
+        conn.close()
+
+    def _init_ltm(self):
+        """Creates or verifies long-term memory table."""
+        os.makedirs(os.path.dirname(self.long_db) or ".", exist_ok=True)
+        conn = sqlite3.connect(self.long_db)
+        c = conn.cursor()
+        c.execute("""
+        CREATE TABLE IF NOT EXISTS long_mem (
+            id TEXT PRIMARY KEY,
+            content TEXT,
+            meta TEXT,
+            created_at REAL
+        )
+        """)
+        conn.commit()
+        conn.close()
+
+    def _init_mem0(self):
+        """Initialize Mem0 client for agent or user memory."""
+        from mem0 import MemoryClient
+        mem_cfg = self.cfg.get("config", {})
+        api_key = mem_cfg.get("api_key", os.getenv("MEM0_API_KEY"))
+        org_id = mem_cfg.get("org_id")
+        proj_id = mem_cfg.get("project_id")
+        if org_id and proj_id:
+            self.mem0_client = MemoryClient(api_key=api_key, org_id=org_id, project_id=proj_id)
+        else:
+            self.mem0_client = MemoryClient(api_key=api_key)
+
+    def _init_chroma(self):
+        """Initialize a local Chroma client for embedding-based search."""
+        try:
+            # Create directory if it doesn't exist
+            rag_path = self.cfg.get("rag_db_path", "chroma_db")
+            os.makedirs(rag_path, exist_ok=True)
+
+            # Initialize ChromaDB with persistent storage
+            self.chroma_client = chromadb.PersistentClient(
+                path=rag_path,
+                settings=ChromaSettings(
+                    anonymized_telemetry=False,
+                    allow_reset=True
+                )
+            )
+
+            collection_name = "memory_store"
+            try:
+                self.chroma_col = self.chroma_client.get_collection(name=collection_name)
+                self._log_verbose("Using existing ChromaDB collection")
+            except Exception as e:
+                self._log_verbose(f"Collection '{collection_name}' not found. Creating new collection. Error: {e}")
+                self.chroma_col = self.chroma_client.create_collection(
+                    name=collection_name,
+                    metadata={"hnsw:space": "cosine"}
+                )
+                self._log_verbose("Created new ChromaDB collection")
+
+        except Exception as e:
+            self._log_verbose(f"Failed to initialize ChromaDB: {e}", logging.ERROR)
+            self.use_rag = False
+
+    # -------------------------------------------------------------------------
+    #                      Basic Quality Score Computation
+    # -------------------------------------------------------------------------
+    def compute_quality_score(
+        self,
+        completeness: float,
+        relevance: float,
+        clarity: float,
+        accuracy: float,
+        weights: Dict[str, float] = None
+    ) -> float:
+        """
+        Combine multiple sub-metrics into one final score, as an example.
+
+        Args:
+            completeness (float): 0-1
+            relevance (float): 0-1
+            clarity (float): 0-1
+            accuracy (float): 0-1
+            weights (Dict[str, float]): optional weighting like {"completeness": 0.25, "relevance": 0.3, ...}
+
+        Returns:
+            float: Weighted average 0-1
+        """
+        if not weights:
+            weights = {
+                "completeness": 0.25,
+                "relevance": 0.25,
+                "clarity": 0.25,
+                "accuracy": 0.25
+            }
+        total = (completeness * weights["completeness"]
+                 + relevance   * weights["relevance"]
+                 + clarity     * weights["clarity"]
+                 + accuracy    * weights["accuracy"]
+                )
+        return round(total, 3)  # e.g. round to 3 decimal places
+
+    # -------------------------------------------------------------------------
+    #                           Short-Term Methods
+    # -------------------------------------------------------------------------
+    def store_short_term(
+        self,
+        text: str,
+        metadata: Dict[str, Any] = None,
+        completeness: float = None,
+        relevance: float = None,
+        clarity: float = None,
+        accuracy: float = None,
+        weights: Dict[str, float] = None,
+        evaluator_quality: float = None
+    ):
+        """Store in short-term memory with optional quality metrics"""
+        logger.info(f"Storing in short-term memory: {text[:100]}...")
+        logger.info(f"Metadata: {metadata}")
+        
+        metadata = self._process_quality_metrics(
+            metadata, completeness, relevance, clarity, 
+            accuracy, weights, evaluator_quality
+        )
+        logger.info(f"Processed metadata: {metadata}")
+        
+        # Existing store logic
+        try:
+            conn = sqlite3.connect(self.short_db)
+            ident = str(time.time_ns())
+            conn.execute(
+                "INSERT INTO short_mem (id, content, meta, created_at) VALUES (?,?,?,?)",
+                (ident, text, json.dumps(metadata), time.time())
+            )
+            conn.commit()
+            conn.close()
+            logger.info(f"Successfully stored in short-term memory with ID: {ident}")
+        except Exception as e:
+            logger.error(f"Failed to store in short-term memory: {e}")
+            raise
+
+    def search_short_term(
+        self, 
+        query: str, 
+        limit: int = 5,
+        min_quality: float = 0.0,
+        relevance_cutoff: float = 0.0
+    ) -> List[Dict[str, Any]]:
+        """Search short-term memory with optional quality filter"""
+        self._log_verbose(f"Searching short memory for: {query}")
+        
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            results = self.mem0_client.search(query=query, limit=limit)
+            filtered = [r for r in results if r.get("score", 1.0) >= relevance_cutoff]
+            return filtered
+            
+        elif self.use_rag and hasattr(self, "chroma_col"):
+            try:
+                from openai import OpenAI
+                client = OpenAI()
+                
+                response = client.embeddings.create(
+                    input=query,
+                    model="text-embedding-3-small"
+                )
+                query_embedding = response.data[0].embedding
+                
+                resp = self.chroma_col.query(
+                    query_embeddings=[query_embedding],
+                    n_results=limit
+                )
+                
+                results = []
+                if resp["ids"]:
+                    for i in range(len(resp["ids"][0])):
+                        metadata = resp["metadatas"][0][i] if "metadatas" in resp else {}
+                        quality = metadata.get("quality", 0.0)
+                        score = 1.0 - (resp["distances"][0][i] if "distances" in resp else 0.0)
+                        if quality >= min_quality and score >= relevance_cutoff:
+                            results.append({
+                                "id": resp["ids"][0][i],
+                                "text": resp["documents"][0][i],
+                                "metadata": metadata,
+                                "score": score
+                            })
+                return results
+            except Exception as e:
+                self._log_verbose(f"Error searching ChromaDB: {e}", logging.ERROR)
+                return []
+        
+        else:
+            # Local fallback
+            conn = sqlite3.connect(self.short_db)
+            c = conn.cursor()
+            rows = c.execute(
+                "SELECT id, content, meta FROM short_mem WHERE content LIKE ? LIMIT ?",
+                (f"%{query}%", limit)
+            ).fetchall()
+            conn.close()
+
+            results = []
+            for row in rows:
+                meta = json.loads(row[2] or "{}")
+                quality = meta.get("quality", 0.0)
+                if quality >= min_quality:
+                    results.append({
+                        "id": row[0],
+                        "text": row[1],
+                        "metadata": meta
+                    })
+            return results
+
+    def reset_short_term(self):
+        """Completely clears short-term memory."""
+        conn = sqlite3.connect(self.short_db)
+        conn.execute("DELETE FROM short_mem")
+        conn.commit()
+        conn.close()
+
+    # -------------------------------------------------------------------------
+    #                           Long-Term Methods
+    # -------------------------------------------------------------------------
+    def _sanitize_metadata(self, metadata: Dict) -> Dict:
+        """Sanitize metadata for ChromaDB - convert to acceptable types"""
+        sanitized = {}
+        for k, v in metadata.items():
+            if v is None:
+                continue
+            if isinstance(v, (str, int, float, bool)):
+                sanitized[k] = v
+            elif isinstance(v, dict):
+                # Convert dict to string representation
+                sanitized[k] = str(v)
+            else:
+                # Convert other types to string
+                sanitized[k] = str(v)
+        return sanitized
+
+    def store_long_term(
+        self,
+        text: str,
+        metadata: Dict[str, Any] = None,
+        completeness: float = None,
+        relevance: float = None,
+        clarity: float = None,
+        accuracy: float = None,
+        weights: Dict[str, float] = None,
+        evaluator_quality: float = None
+    ):
+        """Store in long-term memory with optional quality metrics"""
+        logger.info(f"Storing in long-term memory: {text[:100]}...")
+        logger.info(f"Initial metadata: {metadata}")
+        
+        # Process metadata
+        metadata = metadata or {}
+        metadata = self._process_quality_metrics(
+            metadata, completeness, relevance, clarity,
+            accuracy, weights, evaluator_quality
+        )
+        logger.info(f"Processed metadata: {metadata}")
+        
+        # Generate unique ID
+        ident = str(time.time_ns())
+        created = time.time()
+
+        # Store in SQLite
+        try:
+            conn = sqlite3.connect(self.long_db)
+            conn.execute(
+                "INSERT INTO long_mem (id, content, meta, created_at) VALUES (?,?,?,?)",
+                (ident, text, json.dumps(metadata), created)
+            )
+            conn.commit()
+            conn.close()
+            logger.info(f"Successfully stored in SQLite with ID: {ident}")
+        except Exception as e:
+            logger.error(f"Error storing in SQLite: {e}")
+            return
+
+        # Store in vector database if enabled
+        if self.use_rag and hasattr(self, "chroma_col"):
+            try:
+                from openai import OpenAI
+                client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))  # Ensure API key is correctly set
+                
+                logger.info("Getting embeddings from OpenAI...")
+                logger.debug(f"Embedding input text: {text}")  # Log the input text
+                
+                response = client.embeddings.create(
+                    input=text,
+                    model="text-embedding-3-small"
+                )
+                embedding = response.data[0].embedding
+                logger.info("Successfully got embeddings")
+                logger.debug(f"Received embedding of length: {len(embedding)}")  # Log embedding details
+                
+                # Sanitize metadata for ChromaDB
+                sanitized_metadata = self._sanitize_metadata(metadata)
+                
+                # Store in ChromaDB with embedding
+                self.chroma_col.add(
+                    documents=[text],
+                    metadatas=[sanitized_metadata],
+                    ids=[ident],
+                    embeddings=[embedding]
+                )
+                logger.info(f"Successfully stored in ChromaDB with ID: {ident}")
+            except Exception as e:
+                logger.error(f"Error storing in ChromaDB: {e}")
+        
+        elif self.use_mem0 and hasattr(self, "mem0_client"):
+            try:
+                self.mem0_client.add(text, metadata=metadata)
+                logger.info("Successfully stored in Mem0")
+            except Exception as e:
+                logger.error(f"Error storing in Mem0: {e}")
+
+
+    def search_long_term(
+        self, 
+        query: str, 
+        limit: int = 5, 
+        relevance_cutoff: float = 0.0,
+        min_quality: float = 0.0
+    ) -> List[Dict[str, Any]]:
+        """Search long-term memory with optional quality filter"""
+        self._log_verbose(f"Searching long memory for: {query}")
+        self._log_verbose(f"Min quality: {min_quality}")
+
+        found = []
+
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            results = self.mem0_client.search(query=query, limit=limit)
+            # Filter by quality
+            filtered = [r for r in results if r.get("metadata", {}).get("quality", 0.0) >= min_quality]
+            logger.info(f"Found {len(filtered)} results in Mem0")
+            return filtered
+
+        elif self.use_rag and hasattr(self, "chroma_col"):
+            try:
+                from openai import OpenAI
+                client = OpenAI()
+                
+                # Get query embedding
+                response = client.embeddings.create(
+                    input=query,
+                    model="text-embedding-3-small"  # Using consistent model
+                )
+                query_embedding = response.data[0].embedding
+                
+                # Search ChromaDB with embedding
+                resp = self.chroma_col.query(
+                    query_embeddings=[query_embedding],
+                    n_results=limit,
+                    include=["documents", "metadatas", "distances"]
+                )
+                
+                results = []
+                if resp["ids"]:
+                    for i in range(len(resp["ids"][0])):
+                        metadata = resp["metadatas"][0][i] if "metadatas" in resp else {}
+                        text = resp["documents"][0][i]
+                        # Add memory record citation
+                        text = f"{text} (Memory record: {text})"
+                        found.append({
+                            "id": resp["ids"][0][i],
+                            "text": text,
+                            "metadata": metadata,
+                            "score": 1.0 - (resp["distances"][0][i] if "distances" in resp else 0.0)
+                        })
+                logger.info(f"Found {len(found)} results in ChromaDB")
+
+            except Exception as e:
+                self._log_verbose(f"Error searching ChromaDB: {e}", logging.ERROR)
+
+        # Always try SQLite as fallback or additional source
+        conn = sqlite3.connect(self.long_db)
+        c = conn.cursor()
+        rows = c.execute(
+            "SELECT id, content, meta, created_at FROM long_mem WHERE content LIKE ? LIMIT ?",
+            (f"%{query}%", limit)
+        ).fetchall()
+        conn.close()
+
+        for row in rows:
+            meta = json.loads(row[2] or "{}")
+            text = row[1]
+            # Add memory record citation if not already present
+            if "(Memory record:" not in text:
+                text = f"{text} (Memory record: {text})"
+            # Only add if not already found by ChromaDB/Mem0
+            if not any(f["id"] == row[0] for f in found):
+                found.append({
+                    "id": row[0],
+                    "text": text,
+                    "metadata": meta,
+                    "created_at": row[3]
+                })
+        logger.info(f"Found {len(found)} total results after SQLite")
+
+        results = found
+
+        # Filter by quality if needed
+        if min_quality > 0:
+            self._log_verbose(f"Found {len(results)} initial results")
+            results = [
+                r for r in results 
+                if r.get("metadata", {}).get("quality", 0.0) >= min_quality
+            ]
+            self._log_verbose(f"After quality filter: {len(results)} results")
+
+        # Apply relevance cutoff if specified
+        if relevance_cutoff > 0:
+            results = [r for r in results if r.get("score", 1.0) >= relevance_cutoff]
+            logger.info(f"After relevance filter: {len(results)} results")
+        
+        return results[:limit]
+
+    def reset_long_term(self):
+        """Clear local LTM DB, plus Chroma or mem0 if in use."""
+        conn = sqlite3.connect(self.long_db)
+        conn.execute("DELETE FROM long_mem")
+        conn.commit()
+        conn.close()
+
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            # Mem0 has no universal reset API. Could implement partial or no-op.
+            pass
+        if self.use_rag and hasattr(self, "chroma_client"):
+            self.chroma_client.reset()  # entire DB
+            self._init_chroma()         # re-init fresh
+
+    # -------------------------------------------------------------------------
+    #                       Entity Memory Methods
+    # -------------------------------------------------------------------------
+    def store_entity(self, name: str, type_: str, desc: str, relations: str):
+        """
+        Save entity info in LTM (or mem0/rag). 
+        We'll label the metadata type = entity for easy filtering.
+        """
+        data = f"Entity {name}({type_}): {desc} | relationships: {relations}"
+        self.store_long_term(data, metadata={"category": "entity"})
+
+    def search_entity(self, query: str, limit: int = 5) -> List[Dict[str, Any]]:
+        """
+        Filter to items that have metadata 'category=entity'.
+        """
+        all_hits = self.search_long_term(query, limit=20)  # gather more
+        ents = []
+        for h in all_hits:
+            meta = h.get("metadata") or {}
+            if meta.get("category") == "entity":
+                ents.append(h)
+        return ents[:limit]
+
+    def reset_entity_only(self):
+        """
+        If you only want to drop entity items from LTM, you'd do a custom 
+        delete from local DB where meta LIKE '%category=entity%'. 
+        For brevity, we do a full LTM reset here.
+        """
+        self.reset_long_term()
+
+    # -------------------------------------------------------------------------
+    #                       User Memory Methods
+    # -------------------------------------------------------------------------
+    def store_user_memory(self, user_id: str, text: str, extra: Dict[str, Any] = None):
+        """
+        If mem0 is used, do user-based addition. Otherwise store in LTM with user in metadata.
+        """
+        meta = {"user_id": user_id}
+        if extra:
+            meta.update(extra)
+
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            self.mem0_client.add(text, user_id=user_id, metadata=meta)
+        else:
+            self.store_long_term(text, metadata=meta)
+
+    def search_user_memory(self, user_id: str, query: str, limit: int = 5) -> List[Dict[str, Any]]:
+        """
+        If mem0 is used, pass user_id in. Otherwise fallback to local filter on user in metadata.
+        """
+        if self.use_mem0 and hasattr(self, "mem0_client"):
+            return self.mem0_client.search(query=query, limit=limit, user_id=user_id)
+        else:
+            hits = self.search_long_term(query, limit=20)
+            filtered = []
+            for h in hits:
+                meta = h.get("metadata", {})
+                if meta.get("user_id") == user_id:
+                    filtered.append(h)
+            return filtered[:limit]
+
+    def reset_user_memory(self):
+        """
+        Clear all user-based info. For simplicity, we do a full LTM reset. 
+        Real usage might filter only metadata "user_id".
+        """
+        self.reset_long_term()
+
+    # -------------------------------------------------------------------------
+    #                 Putting it all Together: Task Finalization
+    # -------------------------------------------------------------------------
+    def finalize_task_output(
+        self,
+        content: str,
+        agent_name: str,
+        quality_score: float,
+        threshold: float = 0.7,
+        metrics: Dict[str, Any] = None,
+        task_id: str = None
+    ):
+        """Store task output in memory with appropriate metadata"""
+        logger.info(f"Finalizing task output: {content[:100]}...")
+        logger.info(f"Agent: {agent_name}, Quality: {quality_score}, Threshold: {threshold}")
+        
+        metadata = {
+            "task_id": task_id,
+            "agent": agent_name,
+            "quality": quality_score,
+            "metrics": metrics,
+            "task_type": "output",
+            "stored_at": time.time()
+        }
+        logger.info(f"Prepared metadata: {metadata}")
+        
+        # Always store in short-term memory
+        try:
+            logger.info("Storing in short-term memory...")
+            self.store_short_term(
+                text=content,
+                metadata=metadata
+            )
+            logger.info("Successfully stored in short-term memory")
+        except Exception as e:
+            logger.error(f"Failed to store in short-term memory: {e}")
+        
+        # Store in long-term memory if quality meets threshold
+        if quality_score >= threshold:
+            try:
+                logger.info(f"Quality score {quality_score} >= {threshold}, storing in long-term memory...")
+                self.store_long_term(
+                    text=content,
+                    metadata=metadata
+                )
+                logger.info("Successfully stored in long-term memory")
+            except Exception as e:
+                logger.error(f"Failed to store in long-term memory: {e}")
+        else:
+            logger.info(f"Quality score {quality_score} < {threshold}, skipping long-term storage")
+
+    # -------------------------------------------------------------------------
+    #                 Building Context (Short, Long, Entities, User)
+    # -------------------------------------------------------------------------
+    def build_context_for_task(
+        self,
+        task_descr: str,
+        user_id: Optional[str] = None,
+        additional: str = "",
+        max_items: int = 3
+    ) -> str:
+        """
+        Merges relevant short-term, long-term, entity, user memories
+        into a single text block with deduplication and clean formatting.
+        """
+        q = (task_descr + " " + additional).strip()
+        lines = []
+        seen_contents = set()  # Track unique contents
+
+        def normalize_content(content: str) -> str:
+            """Normalize content for deduplication"""
+            # Extract just the main content without citations for comparison
+            normalized = content.split("(Memory record:")[0].strip()
+            # Keep more characters to reduce false duplicates
+            normalized = ''.join(c.lower() for c in normalized if not c.isspace())
+            return normalized
+
+        def format_content(content: str, max_len: int = 150) -> str:
+            """Format content with clean truncation at word boundaries"""
+            if not content:
+                return ""
+            
+            # Clean up content by removing extra whitespace and newlines
+            content = ' '.join(content.split())
+            
+            # If content contains a memory citation, preserve it
+            if "(Memory record:" in content:
+                return content  # Keep original citation format
+            
+            # Regular content truncation
+            if len(content) <= max_len:
+                return content
+            
+            truncate_at = content.rfind(' ', 0, max_len - 3)
+            if truncate_at == -1:
+                truncate_at = max_len - 3
+            return content[:truncate_at] + "..."
+
+        def add_section(title: str, hits: List[Any]) -> None:
+            """Add a section of memory hits with deduplication"""
+            if not hits:
+                return
+                
+            formatted_hits = []
+            for h in hits:
+                content = h.get('text', '') if isinstance(h, dict) else str(h)
+                if not content:
+                    continue
+                    
+                # Keep original format if it has a citation
+                if "(Memory record:" in content:
+                    formatted = content
+                else:
+                    formatted = format_content(content)
+                
+                # Only add if we haven't seen this normalized content before
+                normalized = normalize_content(formatted)
+                if normalized not in seen_contents:
+                    seen_contents.add(normalized)
+                    formatted_hits.append(formatted)
+            
+            if formatted_hits:
+                # Add section header
+                if lines:
+                    lines.append("")  # Space before new section
+                lines.append(title)
+                lines.append("=" * len(title))  # Underline the title
+                lines.append("")  # Space after title
+                
+                # Add formatted content with bullet points
+                for content in formatted_hits:
+                    lines.append(f" • {content}")
+
+        # Add each section
+        # First get all results
+        short_term = self.search_short_term(q, limit=max_items)
+        long_term = self.search_long_term(q, limit=max_items)
+        entities = self.search_entity(q, limit=max_items)
+        user_mem = self.search_user_memory(user_id, q, limit=max_items) if user_id else []
+
+        # Add sections in order of priority
+        add_section("Short-term Memory Context", short_term)
+        add_section("Long-term Memory Context", long_term)
+        add_section("Entity Context", entities)
+        if user_id:
+            add_section("User Context", user_mem)
+
+        return "\n".join(lines) if lines else ""
+
+    # -------------------------------------------------------------------------
+    #                      Master Reset (Everything)
+    # -------------------------------------------------------------------------
+    def reset_all(self):
+        """
+        Fully wipes short-term, long-term, and any memory in mem0 or rag.
+        """
+        self.reset_short_term()
+        self.reset_long_term()
+        # Entities & user memory are stored in LTM or mem0, so no separate step needed.
+
+    def _process_quality_metrics(
+        self,
+        metadata: Dict[str, Any],
+        completeness: float = None,
+        relevance: float = None,
+        clarity: float = None, 
+        accuracy: float = None,
+        weights: Dict[str, float] = None,
+        evaluator_quality: float = None
+    ) -> Dict[str, Any]:
+        """Process and store quality metrics in metadata"""
+        metadata = metadata or {}
+        
+        # Handle sub-metrics if provided
+        if None not in [completeness, relevance, clarity, accuracy]:
+            metadata.update({
+                "completeness": completeness,
+                "relevance": relevance,
+                "clarity": clarity,
+                "accuracy": accuracy,
+                "quality": self.compute_quality_score(
+                    completeness, relevance, clarity, accuracy, weights
+                )
+            })
+        # Handle external evaluator quality if provided
+        elif evaluator_quality is not None:
+            metadata["quality"] = evaluator_quality
+        
+        return metadata
+
+    def calculate_quality_metrics(
+        self,
+        output: str,
+        expected_output: str,
+        llm: Optional[str] = None,
+        custom_prompt: Optional[str] = None
+    ) -> Dict[str, float]:
+        """Calculate quality metrics using LLM"""
+        logger.info("Calculating quality metrics for output")
+        logger.info(f"Output: {output[:100]}...")
+        logger.info(f"Expected: {expected_output[:100]}...")
+        
+        # Default evaluation prompt
+        default_prompt = f"""
+        Evaluate the following output against expected output.
+        Score each metric from 0.0 to 1.0:
+        - Completeness: Does it address all requirements?
+        - Relevance: Does it match expected output?
+        - Clarity: Is it clear and well-structured?
+        - Accuracy: Is it factually correct?
+
+        Expected: {expected_output}
+        Actual: {output}
+
+        Return ONLY a JSON with these keys: completeness, relevance, clarity, accuracy
+        Example: {{"completeness": 0.95, "relevance": 0.8, "clarity": 0.9, "accuracy": 0.85}}
+        """
+
+        try:
+            # Use OpenAI client from main.py
+            from ..main import client
+            
+            response = client.chat.completions.create(
+                model=llm or "gpt-4o",
+                messages=[{
+                    "role": "user", 
+                    "content": custom_prompt or default_prompt
+                }],
+                response_format={"type": "json_object"},
+                temperature=0.3
+            )
+            
+            metrics = json.loads(response.choices[0].message.content)
+            
+            # Validate metrics
+            required = ["completeness", "relevance", "clarity", "accuracy"]
+            if not all(k in metrics for k in required):
+                raise ValueError("Missing required metrics in LLM response")
+            
+            logger.info(f"Calculated metrics: {metrics}")
+            return metrics
+            
+        except Exception as e:
+            logger.error(f"Error calculating metrics: {e}")
+            return {
+                "completeness": 0.0,
+                "relevance": 0.0,
+                "clarity": 0.0,
+                "accuracy": 0.0
+            }
+
+    def store_quality(
+        self,
+        text: str,
+        quality_score: float,
+        task_id: Optional[str] = None,
+        iteration: Optional[int] = None,
+        metrics: Optional[Dict[str, float]] = None,
+        memory_type: Literal["short", "long"] = "long"
+    ) -> None:
+        """Store quality metrics in memory"""
+        logger.info(f"Attempting to store in {memory_type} memory: {text[:100]}...")
+        
+        metadata = {
+            "quality": quality_score,
+            "task_id": task_id,
+            "iteration": iteration
+        }
+        
+        if metrics:
+            metadata.update({
+                k: v for k, v in metrics.items()  # Remove metric_ prefix
+            })
+            
+        logger.info(f"With metadata: {metadata}")
+        
+        try:
+            if memory_type == "short":
+                self.store_short_term(text, metadata=metadata)
+                logger.info("Successfully stored in short-term memory")
+            else:
+                self.store_long_term(text, metadata=metadata)
+                logger.info("Successfully stored in long-term memory")
+        except Exception as e:
+            logger.error(f"Failed to store in memory: {e}")
+
+    def search_with_quality(
+        self,
+        query: str,
+        min_quality: float = 0.0,
+        memory_type: Literal["short", "long"] = "long",
+        limit: int = 5
+    ) -> List[Dict[str, Any]]:
+        """Search with quality filter"""
+        logger.info(f"Searching {memory_type} memory for: {query}")
+        logger.info(f"Min quality: {min_quality}")
+        
+        search_func = (
+            self.search_short_term if memory_type == "short" 
+            else self.search_long_term
+        )
+        
+        results = search_func(query, limit=limit)
+        logger.info(f"Found {len(results)} initial results")
+        
+        filtered = [
+            r for r in results 
+            if r.get("metadata", {}).get("quality", 0.0) >= min_quality
+        ]
+        logger.info(f"After quality filter: {len(filtered)} results")
+        
+        return filtered

agents/praisonaiagents/process/__init__.py

@@ -0,0 +1,3 @@
+from .process import Process
+
+__all__ = ['Process'] 

agents/praisonaiagents/process/process.py

@@ -0,0 +1,534 @@
+import logging
+import asyncio
+from typing import Dict, Optional, List, Any, AsyncGenerator
+from pydantic import BaseModel
+from ..agent.agent import Agent
+from ..task.task import Task
+from ..main import display_error, client
+
+class LoopItems(BaseModel):
+    items: List[Any]
+
+class Process:
+    def __init__(self, tasks: Dict[str, Task], agents: List[Agent], manager_llm: Optional[str] = None, verbose: bool = False):
+        self.tasks = tasks
+        self.agents = agents
+        self.manager_llm = manager_llm
+        self.verbose = verbose
+
+    async def aworkflow(self) -> AsyncGenerator[str, None]:
+        """Async version of workflow method"""
+        # Build workflow relationships first
+        for task in self.tasks.values():
+            if task.next_tasks:
+                for next_task_name in task.next_tasks:
+                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+                    if next_task:
+                        next_task.previous_tasks.append(task.name)
+
+        # Find start task
+        start_task = None
+        for task_id, task in self.tasks.items():
+            if task.is_start:
+                start_task = task
+                break
+        
+        if not start_task:
+            start_task = list(self.tasks.values())[0]
+            logging.info("No start task marked, using first task")
+        
+        current_task = start_task
+        visited_tasks = set()
+        loop_data = {}  # Store loop-specific data
+        
+        while current_task and current_task.id not in visited_tasks:
+            task_id = current_task.id
+            logging.info(f"Executing workflow task: {current_task.name if current_task.name else task_id}")
+            
+            # Add context from previous tasks to description
+            if current_task.previous_tasks or current_task.context:
+                context = "\nInput data from previous tasks:"
+                
+                # Add data from previous tasks in workflow
+                for prev_name in current_task.previous_tasks:
+                    prev_task = next((t for t in self.tasks.values() if t.name == prev_name), None)
+                    if prev_task and prev_task.result:
+                        # Handle loop data
+                        if current_task.task_type == "loop":
+                            # create a loop manager Agent
+                            loop_manager = Agent(
+                                name="Loop Manager",
+                                role="Loop data processor",
+                                goal="Process loop data and convert it to list format",
+                                backstory="Expert at handling loop data and converting it to proper format",
+                                llm=self.manager_llm,
+                                verbose=self.verbose,
+                                markdown=True
+                            )
+
+                            # get the loop data convert it to list using calling Agent class chat
+                            loop_prompt = f"""
+Process this data into a list format:
+{prev_task.result.raw}
+
+Return a JSON object with an 'items' array containing the items to process.
+"""
+                            if current_task.async_execution:
+                                loop_data_str = await loop_manager.achat(
+                                    prompt=loop_prompt,
+                                    output_json=LoopItems
+                                )
+                            else:
+                                loop_data_str = loop_manager.chat(
+                                    prompt=loop_prompt,
+                                    output_json=LoopItems
+                                )
+                            
+                            try:
+                                # The response will already be parsed into LoopItems model
+                                loop_data[f"loop_{current_task.name}"] = {
+                                    "items": loop_data_str.items,
+                                    "index": 0,
+                                    "remaining": len(loop_data_str.items)
+                                }
+                                context += f"\nCurrent loop item: {loop_data_str.items[0]}"
+                            except Exception as e:
+                                display_error(f"Failed to process loop data: {e}")
+                                context += f"\n{prev_name}: {prev_task.result.raw}"
+                        else:
+                            context += f"\n{prev_name}: {prev_task.result.raw}"
+                
+                # Add data from context tasks
+                if current_task.context:
+                    for ctx_task in current_task.context:
+                        if ctx_task.result and ctx_task.name != current_task.name:
+                            context += f"\n{ctx_task.name}: {ctx_task.result.raw}"
+                
+                # Update task description with context
+                current_task.description = current_task.description + context
+            
+            # Execute task using existing run_task method
+            yield task_id
+            visited_tasks.add(task_id)
+            
+            # Handle loop progression
+            if current_task.task_type == "loop":
+                loop_key = f"loop_{current_task.name}"
+                if loop_key in loop_data:
+                    loop_info = loop_data[loop_key]
+                    loop_info["index"] += 1
+                    has_more = loop_info["remaining"] > 0
+                    
+                    # Update result to trigger correct condition
+                    if current_task.result:
+                        result = current_task.result.raw
+                        if has_more:
+                            result += "\nmore"
+                        else:
+                            result += "\ndone"
+                        current_task.result.raw = result
+                    
+            # Determine next task based on result
+            next_task = None
+            if current_task.result:
+                if current_task.task_type in ["decision", "loop"]:
+                    result = current_task.result.raw.lower()
+                    # Check conditions
+                    for condition, tasks in current_task.condition.items():
+                        if condition.lower() in result and tasks:
+                            next_task_name = tasks[0]
+                            next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+                            # For loops, allow revisiting the same task
+                            if next_task and next_task.id == current_task.id:
+                                visited_tasks.discard(current_task.id)
+                            break
+            
+                if not next_task and current_task.next_tasks:
+                    next_task_name = current_task.next_tasks[0]
+                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+            
+            current_task = next_task
+            if not current_task:
+                logging.info("Workflow execution completed")
+                break
+
+    async def asequential(self) -> AsyncGenerator[str, None]:
+        """Async version of sequential method"""
+        for task_id in self.tasks:
+            if self.tasks[task_id].status != "completed":
+                yield task_id
+
+    async def ahierarchical(self) -> AsyncGenerator[str, None]:
+        """Async version of hierarchical method"""
+        logging.debug(f"Starting hierarchical task execution with {len(self.tasks)} tasks")
+        manager_agent = Agent(
+            name="Manager",
+            role="Project manager",
+            goal="Manage the entire flow of tasks and delegate them to the right agent",
+            backstory="Expert project manager to coordinate tasks among agents",
+            llm=self.manager_llm,
+            verbose=self.verbose,
+            markdown=True,
+            self_reflect=False
+        )
+
+        class ManagerInstructions(BaseModel):
+            task_id: int
+            agent_name: str
+            action: str
+
+        manager_task = Task(
+            name="manager_task",
+            description="Decide the order of tasks and which agent executes them",
+            expected_output="All tasks completed successfully",
+            agent=manager_agent
+        )
+        manager_task_id = yield manager_task
+        logging.info(f"Created manager task with ID {manager_task_id}")
+
+        completed_count = 0
+        total_tasks = len(self.tasks) - 1
+        logging.info(f"Need to complete {total_tasks} tasks (excluding manager task)")
+
+        while completed_count < total_tasks:
+            tasks_summary = []
+            for tid, tk in self.tasks.items():
+                if tk.name == "manager_task":
+                    continue
+                task_info = {
+                    "task_id": tid,
+                    "name": tk.name,
+                    "description": tk.description,
+                    "status": tk.status if tk.status else "not started",
+                    "agent": tk.agent.name if tk.agent else "No agent"
+                }
+                tasks_summary.append(task_info)
+                logging.info(f"Task {tid} status: {task_info}")
+
+            manager_prompt = f"""
+Here is the current status of all tasks except yours (manager_task):
+{tasks_summary}
+
+Provide a JSON with the structure:
+{{
+   "task_id": <int>,
+   "agent_name": "<string>",
+   "action": "<execute or stop>"
+}}
+"""
+
+            try:
+                logging.info("Requesting manager instructions...")
+                if manager_task.async_execution:
+                    manager_response = await client.beta.chat.completions.parse(
+                        model=self.manager_llm,
+                        messages=[
+                            {"role": "system", "content": manager_task.description},
+                            {"role": "user", "content": manager_prompt}
+                        ],
+                        temperature=0.7,
+                        response_format=ManagerInstructions
+                    )
+                else:
+                    manager_response = client.beta.chat.completions.parse(
+                        model=self.manager_llm,
+                        messages=[
+                            {"role": "system", "content": manager_task.description},
+                            {"role": "user", "content": manager_prompt}
+                        ],
+                        temperature=0.7,
+                        response_format=ManagerInstructions
+                    )
+                parsed_instructions = manager_response.choices[0].message.parsed
+                logging.info(f"Manager instructions: {parsed_instructions}")
+            except Exception as e:
+                display_error(f"Manager parse error: {e}")
+                logging.error(f"Manager parse error: {str(e)}", exc_info=True)
+                break
+
+            selected_task_id = parsed_instructions.task_id
+            selected_agent_name = parsed_instructions.agent_name
+            action = parsed_instructions.action
+
+            logging.info(f"Manager selected task_id={selected_task_id}, agent={selected_agent_name}, action={action}")
+
+            if action.lower() == "stop":
+                logging.info("Manager decided to stop task execution")
+                break
+
+            if selected_task_id not in self.tasks:
+                error_msg = f"Manager selected invalid task id {selected_task_id}"
+                display_error(error_msg)
+                logging.error(error_msg)
+                break
+
+            original_agent = self.tasks[selected_task_id].agent.name if self.tasks[selected_task_id].agent else "None"
+            for a in self.agents:
+                if a.name == selected_agent_name:
+                    self.tasks[selected_task_id].agent = a
+                    logging.info(f"Changed agent for task {selected_task_id} from {original_agent} to {selected_agent_name}")
+                    break
+
+            if self.tasks[selected_task_id].status != "completed":
+                logging.info(f"Starting execution of task {selected_task_id}")
+                yield selected_task_id
+                logging.info(f"Finished execution of task {selected_task_id}, status: {self.tasks[selected_task_id].status}")
+
+            if self.tasks[selected_task_id].status == "completed":
+                completed_count += 1
+                logging.info(f"Task {selected_task_id} completed. Total completed: {completed_count}/{total_tasks}")
+
+        self.tasks[manager_task.id].status = "completed"
+        if self.verbose >= 1:
+            logging.info("All tasks completed under manager supervision.")
+        logging.info("Hierarchical task execution finished") 
+
+    def workflow(self):
+        """Synchronous version of workflow method"""
+        # Build workflow relationships first
+        for task in self.tasks.values():
+            if task.next_tasks:
+                for next_task_name in task.next_tasks:
+                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+                    if next_task:
+                        next_task.previous_tasks.append(task.name)
+
+        # Find start task
+        start_task = None
+        for task_id, task in self.tasks.items():
+            if task.is_start:
+                start_task = task
+                break
+        
+        if not start_task:
+            start_task = list(self.tasks.values())[0]
+            logging.info("No start task marked, using first task")
+        
+        current_task = start_task
+        visited_tasks = set()
+        loop_data = {}  # Store loop-specific data
+        
+        while current_task and current_task.id not in visited_tasks:
+            task_id = current_task.id
+            logging.info(f"Executing workflow task: {current_task.name if current_task.name else task_id}")
+            
+            # Add context from previous tasks to description
+            if current_task.previous_tasks or current_task.context:
+                context = "\nInput data from previous tasks:"
+                
+                # Add data from previous tasks in workflow
+                for prev_name in current_task.previous_tasks:
+                    prev_task = next((t for t in self.tasks.values() if t.name == prev_name), None)
+                    if prev_task and prev_task.result:
+                        # Handle loop data
+                        if current_task.task_type == "loop":
+                            # create a loop manager Agent
+                            loop_manager = Agent(
+                                name="Loop Manager",
+                                role="Loop data processor",
+                                goal="Process loop data and convert it to list format",
+                                backstory="Expert at handling loop data and converting it to proper format",
+                                llm=self.manager_llm,
+                                verbose=self.verbose,
+                                markdown=True
+                            )
+
+                            # get the loop data convert it to list using calling Agent class chat
+                            loop_prompt = f"""
+Process this data into a list format:
+{prev_task.result.raw}
+
+Return a JSON object with an 'items' array containing the items to process.
+"""
+                            loop_data_str = loop_manager.chat(
+                                prompt=loop_prompt,
+                                output_json=LoopItems
+                            )
+                            
+                            try:
+                                # The response will already be parsed into LoopItems model
+                                loop_data[f"loop_{current_task.name}"] = {
+                                    "items": loop_data_str.items,
+                                    "index": 0,
+                                    "remaining": len(loop_data_str.items)
+                                }
+                                context += f"\nCurrent loop item: {loop_data_str.items[0]}"
+                            except Exception as e:
+                                display_error(f"Failed to process loop data: {e}")
+                                context += f"\n{prev_name}: {prev_task.result.raw}"
+                        else:
+                            context += f"\n{prev_name}: {prev_task.result.raw}"
+                
+                # Add data from context tasks
+                if current_task.context:
+                    for ctx_task in current_task.context:
+                        if ctx_task.result and ctx_task.name != current_task.name:
+                            context += f"\n{ctx_task.name}: {ctx_task.result.raw}"
+                
+                # Update task description with context
+                current_task.description = current_task.description + context
+            
+            # Execute task using existing run_task method
+            yield task_id
+            visited_tasks.add(task_id)
+            
+            # Handle loop progression
+            if current_task.task_type == "loop":
+                loop_key = f"loop_{current_task.name}"
+                if loop_key in loop_data:
+                    loop_info = loop_data[loop_key]
+                    loop_info["index"] += 1
+                    has_more = loop_info["remaining"] > 0
+                    
+                    # Update result to trigger correct condition
+                    if current_task.result:
+                        result = current_task.result.raw
+                        if has_more:
+                            result += "\nmore"
+                        else:
+                            result += "\ndone"
+                        current_task.result.raw = result
+                    
+            # Determine next task based on result
+            next_task = None
+            if current_task.result:
+                if current_task.task_type in ["decision", "loop"]:
+                    result = current_task.result.raw.lower()
+                    # Check conditions
+                    for condition, tasks in current_task.condition.items():
+                        if condition.lower() in result and tasks:
+                            next_task_name = tasks[0]
+                            next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+                            # For loops, allow revisiting the same task
+                            if next_task and next_task.id == current_task.id:
+                                visited_tasks.discard(current_task.id)
+                            break
+            
+                if not next_task and current_task.next_tasks:
+                    next_task_name = current_task.next_tasks[0]
+                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+            
+            current_task = next_task
+            if not current_task:
+                logging.info("Workflow execution completed")
+                break
+
+    def sequential(self):
+        """Synchronous version of sequential method"""
+        for task_id in self.tasks:
+            if self.tasks[task_id].status != "completed":
+                yield task_id
+
+    def hierarchical(self):
+        """Synchronous version of hierarchical method"""
+        logging.debug(f"Starting hierarchical task execution with {len(self.tasks)} tasks")
+        manager_agent = Agent(
+            name="Manager",
+            role="Project manager",
+            goal="Manage the entire flow of tasks and delegate them to the right agent",
+            backstory="Expert project manager to coordinate tasks among agents",
+            llm=self.manager_llm,
+            verbose=self.verbose,
+            markdown=True,
+            self_reflect=False
+        )
+
+        class ManagerInstructions(BaseModel):
+            task_id: int
+            agent_name: str
+            action: str
+
+        manager_task = Task(
+            name="manager_task",
+            description="Decide the order of tasks and which agent executes them",
+            expected_output="All tasks completed successfully",
+            agent=manager_agent
+        )
+        manager_task_id = yield manager_task
+        logging.info(f"Created manager task with ID {manager_task_id}")
+
+        completed_count = 0
+        total_tasks = len(self.tasks) - 1
+        logging.info(f"Need to complete {total_tasks} tasks (excluding manager task)")
+
+        while completed_count < total_tasks:
+            tasks_summary = []
+            for tid, tk in self.tasks.items():
+                if tk.name == "manager_task":
+                    continue
+                task_info = {
+                    "task_id": tid,
+                    "name": tk.name,
+                    "description": tk.description,
+                    "status": tk.status if tk.status else "not started",
+                    "agent": tk.agent.name if tk.agent else "No agent"
+                }
+                tasks_summary.append(task_info)
+                logging.info(f"Task {tid} status: {task_info}")
+
+            manager_prompt = f"""
+Here is the current status of all tasks except yours (manager_task):
+{tasks_summary}
+
+Provide a JSON with the structure:
+{{
+   "task_id": <int>,
+   "agent_name": "<string>",
+   "action": "<execute or stop>"
+}}
+"""
+
+            try:
+                logging.info("Requesting manager instructions...")
+                manager_response = client.beta.chat.completions.parse(
+                    model=self.manager_llm,
+                    messages=[
+                        {"role": "system", "content": manager_task.description},
+                        {"role": "user", "content": manager_prompt}
+                    ],
+                    temperature=0.7,
+                    response_format=ManagerInstructions
+                )
+                parsed_instructions = manager_response.choices[0].message.parsed
+                logging.info(f"Manager instructions: {parsed_instructions}")
+            except Exception as e:
+                display_error(f"Manager parse error: {e}")
+                logging.error(f"Manager parse error: {str(e)}", exc_info=True)
+                break
+
+            selected_task_id = parsed_instructions.task_id
+            selected_agent_name = parsed_instructions.agent_name
+            action = parsed_instructions.action
+
+            logging.info(f"Manager selected task_id={selected_task_id}, agent={selected_agent_name}, action={action}")
+
+            if action.lower() == "stop":
+                logging.info("Manager decided to stop task execution")
+                break
+
+            if selected_task_id not in self.tasks:
+                error_msg = f"Manager selected invalid task id {selected_task_id}"
+                display_error(error_msg)
+                logging.error(error_msg)
+                break
+
+            original_agent = self.tasks[selected_task_id].agent.name if self.tasks[selected_task_id].agent else "None"
+            for a in self.agents:
+                if a.name == selected_agent_name:
+                    self.tasks[selected_task_id].agent = a
+                    logging.info(f"Changed agent for task {selected_task_id} from {original_agent} to {selected_agent_name}")
+                    break
+
+            if self.tasks[selected_task_id].status != "completed":
+                logging.info(f"Starting execution of task {selected_task_id}")
+                yield selected_task_id
+                logging.info(f"Finished execution of task {selected_task_id}, status: {self.tasks[selected_task_id].status}")
+
+            if self.tasks[selected_task_id].status == "completed":
+                completed_count += 1
+                logging.info(f"Task {selected_task_id} completed. Total completed: {completed_count}/{total_tasks}")
+
+        self.tasks[manager_task.id].status = "completed"
+        if self.verbose >= 1:
+            logging.info("All tasks completed under manager supervision.")
+        logging.info("Hierarchical task execution finished") 

agents/praisonaiagents/task/task.py

@@ -1,14 +1,21 @@
 import logging
-from typing import List, Optional, Dict, Any, Type
+import asyncio
+from typing import List, Optional, Dict, Any, Type, Callable, Union, Coroutine
 from pydantic import BaseModel
 from ..main import TaskOutput
 from ..agent.agent import Agent
+import uuid
+import os
+import time
+
+# Set up logger
+logger = logging.getLogger(__name__)
 
 class Task:
     def __init__(
         self,
         description: str,
-        expected_output: str,
+        expected_output: Optional[str] = None,
         agent: Optional[Agent] = None,
         name: Optional[str] = None,
         tools: Optional[List[Any]] = None,
@@ -18,15 +25,24 @@ class Task:
         output_file: Optional[str] = None,
         output_json: Optional[Type[BaseModel]] = None,
         output_pydantic: Optional[Type[BaseModel]] = None,
-        callback: Optional[Any] = None,
+        callback: Optional[Union[Callable[[TaskOutput], Any], Callable[[TaskOutput], Coroutine[Any, Any, Any]]]] = None,
         status: str = "not started",
         result: Optional[TaskOutput] = None,
         create_directory: Optional[bool] = False,
-        id: Optional[int] = None
+        id: Optional[int] = None,
+        images: Optional[List[str]] = None,
+        next_tasks: Optional[List[str]] = None,
+        task_type: str = "task",
+        condition: Optional[Dict[str, List[str]]] = None,
+        is_start: bool = False,
+        loop_state: Optional[Dict[str, Union[str, int]]] = None,
+        memory=None,
+        quality_check=True
     ):
-        self.description = description
-        self.expected_output = expected_output
+        self.id = str(uuid.uuid4()) if id is None else str(id)
         self.name = name
+        self.description = description
+        self.expected_output = expected_output if expected_output is not None else "Complete the task successfully"
         self.agent = agent
         self.tools = tools if tools else []
         self.context = context if context else []
@@ -39,10 +55,168 @@ class Task:
         self.status = status
         self.result = result
         self.create_directory = create_directory
-        self.id = id
+        self.images = images if images else []
+        self.next_tasks = next_tasks if next_tasks else []
+        self.task_type = task_type
+        self.condition = condition if condition else {}
+        self.is_start = is_start
+        self.loop_state = loop_state if loop_state else {}
+        self.memory = memory
+        self.quality_check = quality_check
+
+        # Set logger level based on config verbose level
+        verbose = self.config.get("verbose", 0)
+        if verbose >= 5:
+            logger.setLevel(logging.INFO)
+        else:
+            logger.setLevel(logging.WARNING)
+            
+        # Also set third-party loggers to WARNING
+        logging.getLogger('chromadb').setLevel(logging.WARNING)
+        logging.getLogger('openai').setLevel(logging.WARNING)
+        logging.getLogger('httpx').setLevel(logging.WARNING)
+        logging.getLogger('httpcore').setLevel(logging.WARNING)
 
         if self.output_json and self.output_pydantic:
             raise ValueError("Only one output type can be defined")
 
+        # Track previous tasks based on next_tasks relationships
+        self.previous_tasks = []
+
     def __str__(self):
-        return f"Task(name='{self.name if self.name else 'None'}', description='{self.description}', agent='{self.agent.name if self.agent else 'None'}', status='{self.status}')" 
+        return f"Task(name='{self.name if self.name else 'None'}', description='{self.description}', agent='{self.agent.name if self.agent else 'None'}', status='{self.status}')"
+
+    def initialize_memory(self):
+        """Initialize memory if config exists but memory doesn't"""
+        if not self.memory and self.config.get('memory_config'):
+            try:
+                from ..memory.memory import Memory
+                logger.info(f"Task {self.id}: Initializing memory from config: {self.config['memory_config']}")
+                self.memory = Memory(config=self.config['memory_config'])
+                logger.info(f"Task {self.id}: Memory initialized successfully")
+                
+                # Verify database was created
+                if os.path.exists(self.config['memory_config']['storage']['path']):
+                    logger.info(f"Task {self.id}: Memory database exists after initialization")
+                else:
+                    logger.error(f"Task {self.id}: Failed to create memory database!")
+                return self.memory
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to initialize memory: {e}")
+                logger.exception(e)
+        return None
+
+    def store_in_memory(self, content: str, agent_name: str = None, task_id: str = None):
+        """Store content in memory with metadata"""
+        if self.memory:
+            try:
+                logger.info(f"Task {self.id}: Storing content in memory...")
+                self.memory.store_long_term(
+                    text=content,
+                    metadata={
+                        "agent_name": agent_name or "Agent",
+                        "task_id": task_id or self.id,
+                        "timestamp": time.time()
+                    }
+                )
+                logger.info(f"Task {self.id}: Content stored in memory")
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to store content in memory: {e}")
+                logger.exception(e)
+
+    async def execute_callback(self, task_output: TaskOutput) -> None:
+        """Execute callback and store quality metrics if enabled"""
+        logger.info(f"Task {self.id}: execute_callback called")
+        logger.info(f"Quality check enabled: {self.quality_check}")
+        
+        # Initialize memory if not already initialized
+        if not self.memory:
+            self.memory = self.initialize_memory()
+        
+        logger.info(f"Memory object exists: {self.memory is not None}")
+        if self.memory:
+            logger.info(f"Memory config: {self.memory.cfg}")
+            # Store task output in memory
+            try:
+                logger.info(f"Task {self.id}: Storing task output in memory...")
+                self.store_in_memory(
+                    content=task_output.raw,
+                    agent_name=self.agent.name if self.agent else "Agent",
+                    task_id=self.id
+                )
+                logger.info(f"Task {self.id}: Task output stored in memory")
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to store task output in memory: {e}")
+                logger.exception(e)
+        
+        logger.info(f"Task output: {task_output.raw[:100]}...")
+        
+        if self.quality_check and self.memory:
+            try:
+                logger.info(f"Task {self.id}: Starting memory operations")
+                logger.info(f"Task {self.id}: Calculating quality metrics for output: {task_output.raw[:100]}...")
+                
+                # Get quality metrics from LLM
+                metrics = self.memory.calculate_quality_metrics(
+                    task_output.raw,
+                    self.expected_output
+                )
+                logger.info(f"Task {self.id}: Quality metrics calculated: {metrics}")
+                
+                quality_score = metrics.get("accuracy", 0.0)
+                logger.info(f"Task {self.id}: Quality score: {quality_score}")
+                
+                # Store in both short and long-term memory with higher threshold
+                logger.info(f"Task {self.id}: Finalizing task output in memory...")
+                self.memory.finalize_task_output(
+                    content=task_output.raw,
+                    agent_name=self.agent.name if self.agent else "Agent",
+                    quality_score=quality_score,
+                    threshold=0.7,  # Only high quality outputs in long-term memory
+                    metrics=metrics,
+                    task_id=self.id
+                )
+                logger.info(f"Task {self.id}: Finalized task output in memory")
+                
+                # Store quality metrics separately
+                logger.info(f"Task {self.id}: Storing quality metrics...")
+                self.memory.store_quality(
+                    text=task_output.raw,
+                    quality_score=quality_score,
+                    task_id=self.id,
+                    metrics=metrics
+                )
+                
+                # Store in both short and long-term memory with higher threshold
+                self.memory.finalize_task_output(
+                    content=task_output.raw,
+                    agent_name=self.agent.name if self.agent else "Agent",
+                    quality_score=quality_score,
+                    threshold=0.7  # Only high quality outputs in long-term memory
+                )
+                
+                # Build context for next tasks
+                if self.next_tasks:
+                    logger.info(f"Task {self.id}: Building context for next tasks...")
+                    context = self.memory.build_context_for_task(
+                        task_descr=task_output.raw,
+                        max_items=5
+                    )
+                    logger.info(f"Task {self.id}: Built context for next tasks: {len(context)} items")
+                
+                logger.info(f"Task {self.id}: Memory operations complete")
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to process memory operations: {e}")
+                logger.exception(e)  # Print full stack trace
+                # Continue execution even if memory operations fail
+
+        # Execute original callback
+        if self.callback:
+            try:
+                if asyncio.iscoroutinefunction(self.callback):
+                    await self.callback(task_output)
+                else:
+                    self.callback(task_output)
+            except Exception as e:
+                logger.error(f"Task {self.id}: Failed to execute callback: {e}")
+                logger.exception(e)

agents/praisonaiagents/tools/README.md

@@ -0,0 +1,149 @@
+# PraisonAI Tools Guide
+
+Welcome to the PraisonAI Tools directory! This guide will help you understand how our tools work and how to create new ones, whether you're a beginner or an experienced programmer.
+
+## What is a Tool?
+
+A tool is a piece of code that helps our AI agents perform specific tasks. Think of tools as special abilities that we give to our agents. For example:
+- An internet search tool lets agents search the web
+- A stock market tool lets agents check stock prices
+- A weather tool lets agents check the weather
+
+## Creating New Tools: The Two Approaches
+
+### 1. Function-Based Approach (Simple Tools)
+
+Best for simple tools that do one specific thing. Like a calculator that just adds numbers.
+
+**When to use:**
+- Tool does one simple task
+- Doesn't need to remember information between uses
+- Doesn't need to share information with other tools
+- Quick, one-time operations
+
+**Example:**
+```python
+def internet_search(query: str):
+    # Search the internet and return results
+    return search_results
+```
+
+**Usage:**
+```python
+from praisonaiagents.tools import internet_search
+
+results = internet_search("AI news")
+```
+
+### 2. Class-Based Approach (Complex Tools)
+
+Best for tools that do multiple related things or need to remember information. Like a smart calculator that remembers your previous calculations and can do many different math operations.
+
+**When to use:**
+- Tool has multiple related functions
+- Needs to remember or share information
+- Needs to manage resources efficiently
+- Has complex setup requirements
+
+**Example:**
+```python
+class StockTools:
+    def get_stock_price(self, symbol):
+        # Get current stock price
+        return price
+        
+    def get_stock_info(self, symbol):
+        # Get detailed stock information
+        return info
+```
+
+**Usage:**
+```python
+from praisonaiagents.tools import get_stock_price, get_stock_info
+
+price = get_stock_price("AAPL")
+info = get_stock_info("AAPL")
+```
+
+## How to Choose Your Approach
+
+Ask yourself these questions:
+
+1. **Is your tool doing one simple thing?**
+   - Yes → Use Function-Based Approach
+   - No → Consider Class-Based Approach
+
+2. **Does your tool need to remember information?**
+   - Yes → Use Class-Based Approach
+   - No → Use Function-Based Approach
+
+3. **Are your tool's operations related to each other?**
+   - Yes → Use Class-Based Approach
+   - No → Use Function-Based Approach
+
+4. **Does your tool need to manage resources efficiently?**
+   - Yes → Use Class-Based Approach
+   - No → Use Function-Based Approach
+
+## Real-World Examples
+
+### Internet Search Tool (Function-Based)
+- Does one thing: searches the internet
+- Doesn't need to remember previous searches
+- Each search is independent
+- Simple input/output operation
+
+### Stock Market Tool (Class-Based)
+- Does multiple things: check prices, get company info, get historical data
+- Remembers stock information to avoid repeated downloads
+- Operations are related (all about stocks)
+- Manages connections efficiently
+
+## Getting Started
+
+1. **Choose Your Approach** based on the guidelines above
+
+2. **Create Your Tool File**:
+   - Name it descriptively (e.g., `weather_tools.py`)
+   - Place it in the `praisonaiagents/tools` directory
+
+3. **Write Your Tool**:
+   - Add clear documentation
+   - Include type hints for better understanding
+   - Handle errors gracefully
+
+4. **Test Your Tool**:
+   - Make sure it works as expected
+   - Test error cases
+   - Check performance
+
+## Best Practices
+
+1. **Documentation**:
+   - Explain what your tool does
+   - Provide examples
+   - List any requirements
+
+2. **Error Handling**:
+   - Always handle possible errors
+   - Return helpful error messages
+   - Don't let your tool crash
+
+3. **Performance**:
+   - Keep it efficient
+   - Don't waste resources
+   - Cache when helpful
+
+4. **User-Friendly**:
+   - Make it easy to use
+   - Use clear function/method names
+   - Keep it simple
+
+## Need Help?
+
+- Check existing tools for examples
+- Ask in our community
+- Read the documentation
+- Don't hesitate to ask questions!
+
+Remember: The goal is to make tools that are easy to use and maintain. Choose the approach that makes the most sense for your specific tool's needs.

agents/praisonaiagents/tools/__init__.py

@@ -0,0 +1,178 @@
+"""Tools package for PraisonAI Agents"""
+from importlib import import_module
+from typing import Any
+
+# Map of function names to their module and class (if any)
+TOOL_MAPPINGS = {
+    # Direct functions
+    'internet_search': ('.duckduckgo_tools', None),
+    'duckduckgo': ('.duckduckgo_tools', None),
+    
+    # arXiv Tools
+    'search_arxiv': ('.arxiv_tools', None),
+    'get_arxiv_paper': ('.arxiv_tools', None),
+    'get_papers_by_author': ('.arxiv_tools', None),
+    'get_papers_by_category': ('.arxiv_tools', None),
+    'arxiv_tools': ('.arxiv_tools', None),
+    
+    # Wikipedia Tools
+    'wiki_search': ('.wikipedia_tools', None),
+    'wiki_summary': ('.wikipedia_tools', None),
+    'wiki_page': ('.wikipedia_tools', None),
+    'wiki_random': ('.wikipedia_tools', None),
+    'wiki_language': ('.wikipedia_tools', None),
+    'wikipedia_tools': ('.wikipedia_tools', None),
+    
+    # Newspaper Tools
+    'get_article': ('.newspaper_tools', None),
+    'get_news_sources': ('.newspaper_tools', None),
+    'get_articles_from_source': ('.newspaper_tools', None),
+    'get_trending_topics': ('.newspaper_tools', None),
+    'newspaper_tools': ('.newspaper_tools', None),
+    
+    # Spider Tools
+    'scrape_page': ('.spider_tools', None),
+    'extract_links': ('.spider_tools', None),
+    'crawl': ('.spider_tools', None),
+    'extract_text': ('.spider_tools', None),
+    'spider_tools': ('.spider_tools', None),
+    
+    # DuckDB Tools
+    'query': ('.duckdb_tools', None),
+    'create_table': ('.duckdb_tools', None),
+    'load_data': ('.duckdb_tools', None),
+    'export_data': ('.duckdb_tools', None),
+    'get_table_info': ('.duckdb_tools', None),
+    'analyze_data': ('.duckdb_tools', None),
+    'duckdb_tools': ('.duckdb_tools', None),
+    
+    # Shell Tools
+    'execute_command': ('.shell_tools', None),
+    'list_processes': ('.shell_tools', None),
+    'kill_process': ('.shell_tools', None),
+    'get_system_info': ('.shell_tools', None),
+    'shell_tools': ('.shell_tools', None),
+
+    # Calculator Tools
+    'evaluate': ('.calculator_tools', None),
+    'solve_equation': ('.calculator_tools', None),
+    'convert_units': ('.calculator_tools', None),
+    'calculate_statistics': ('.calculator_tools', None),
+    'calculate_financial': ('.calculator_tools', None),
+    'calculator_tools': ('.calculator_tools', None),
+
+    # Class methods from YFinance
+    'get_stock_price': ('.yfinance_tools', 'YFinanceTools'),
+    'get_stock_info': ('.yfinance_tools', 'YFinanceTools'),
+    'get_historical_data': ('.yfinance_tools', 'YFinanceTools'),
+    'yfinance': ('.yfinance_tools', 'YFinanceTools'),
+
+    # File Tools
+    'read_file': ('.file_tools', 'FileTools'),
+    'write_file': ('.file_tools', 'FileTools'),
+    'list_files': ('.file_tools', 'FileTools'),
+    'get_file_info': ('.file_tools', 'FileTools'),
+    'copy_file': ('.file_tools', 'FileTools'),
+    'move_file': ('.file_tools', 'FileTools'),
+    'delete_file': ('.file_tools', 'FileTools'),
+    'file_tools': ('.file_tools', 'FileTools'),
+
+    # CSV Tools
+    'read_csv': ('.csv_tools', 'CSVTools'),
+    'write_csv': ('.csv_tools', 'CSVTools'),
+    'merge_csv': ('.csv_tools', 'CSVTools'),
+    'analyze_csv': ('.csv_tools', 'CSVTools'),
+    'split_csv': ('.csv_tools', 'CSVTools'),
+    'csv_tools': ('.csv_tools', 'CSVTools'),
+
+    # JSON Tools
+    'read_json': ('.json_tools', 'JSONTools'),
+    'write_json': ('.json_tools', 'JSONTools'),
+    'merge_json': ('.json_tools', 'JSONTools'),
+    'validate_json': ('.json_tools', 'JSONTools'),
+    'analyze_json': ('.json_tools', 'JSONTools'),
+    'transform_json': ('.json_tools', 'JSONTools'),
+    'json_tools': ('.json_tools', 'JSONTools'),
+
+    # Excel Tools
+    'read_excel': ('.excel_tools', 'ExcelTools'),
+    'write_excel': ('.excel_tools', 'ExcelTools'),
+    'merge_excel': ('.excel_tools', 'ExcelTools'),
+    'create_chart': ('.excel_tools', 'ExcelTools'),
+    'add_chart_to_sheet': ('.excel_tools', 'ExcelTools'),
+    'excel_tools': ('.excel_tools', 'ExcelTools'),
+
+    # XML Tools
+    'read_xml': ('.xml_tools', 'XMLTools'),
+    'write_xml': ('.xml_tools', 'XMLTools'),
+    'transform_xml': ('.xml_tools', 'XMLTools'),
+    'validate_xml': ('.xml_tools', 'XMLTools'),
+    'xml_to_dict': ('.xml_tools', 'XMLTools'),
+    'dict_to_xml': ('.xml_tools', 'XMLTools'),
+    'xpath_query': ('.xml_tools', 'XMLTools'),
+    'xml_tools': ('.xml_tools', 'XMLTools'),
+
+    # YAML Tools
+    'read_yaml': ('.yaml_tools', 'YAMLTools'),
+    'write_yaml': ('.yaml_tools', 'YAMLTools'),
+    'merge_yaml': ('.yaml_tools', 'YAMLTools'),
+    'validate_yaml': ('.yaml_tools', 'YAMLTools'),
+    'analyze_yaml': ('.yaml_tools', 'YAMLTools'),
+    'transform_yaml': ('.yaml_tools', 'YAMLTools'),
+    'yaml_tools': ('.yaml_tools', 'YAMLTools'),
+
+    # Python Tools
+    'execute_code': ('.python_tools', 'PythonTools'),
+    'analyze_code': ('.python_tools', 'PythonTools'),
+    'format_code': ('.python_tools', 'PythonTools'),
+    'lint_code': ('.python_tools', 'PythonTools'),
+    'disassemble_code': ('.python_tools', 'PythonTools'),
+    'python_tools': ('.python_tools', 'PythonTools'),
+
+    # Pandas Tools
+    'filter_data': ('.pandas_tools', 'PandasTools'),
+    'get_summary': ('.pandas_tools', 'PandasTools'),
+    'group_by': ('.pandas_tools', 'PandasTools'),
+    'pivot_table': ('.pandas_tools', 'PandasTools'),
+    'pandas_tools': ('.pandas_tools', 'PandasTools'),
+}
+
+_instances = {}  # Cache for class instances
+
+def __getattr__(name: str) -> Any:
+    """Smart lazy loading of tools with class method support."""
+    if name not in TOOL_MAPPINGS:
+        raise AttributeError(f"module '{__package__}' has no attribute '{name}'")
+    
+    module_path, class_name = TOOL_MAPPINGS[name]
+    
+    if class_name is None:
+        # Direct function import
+        module = import_module(module_path, __package__)
+        if name in [
+            'duckduckgo', 'internet_search',
+            'search_arxiv', 'get_arxiv_paper', 'get_papers_by_author', 'get_papers_by_category',
+            'wiki_search', 'wiki_summary', 'wiki_page', 'wiki_random', 'wiki_language',
+            'get_article', 'get_news_sources', 'get_articles_from_source', 'get_trending_topics',
+            'scrape_page', 'extract_links', 'crawl', 'extract_text',
+            'query', 'create_table', 'load_data', 'export_data', 'get_table_info', 'analyze_data',
+            'execute_command', 'list_processes', 'kill_process', 'get_system_info',
+            'evaluate', 'solve_equation', 'convert_units', 'calculate_statistics', 'calculate_financial'
+        ]:
+            return getattr(module, name)
+        if name in ['file_tools', 'pandas_tools', 'wikipedia_tools',
+                   'newspaper_tools', 'arxiv_tools', 'spider_tools', 'duckdb_tools', 'csv_tools', 'json_tools', 'excel_tools', 'xml_tools', 'yaml_tools', 'calculator_tools', 'python_tools', 'shell_tools']:
+            return module  # Returns the callable module
+        return getattr(module, name)
+    else:
+        # Class method import
+        if class_name not in _instances:
+            module = import_module(module_path, __package__)
+            class_ = getattr(module, class_name)
+            _instances[class_name] = class_()
+        
+        # Get the method and bind it to the instance
+        method = getattr(_instances[class_name], name)
+        return method
+
+__all__ = list(TOOL_MAPPINGS.keys())

agents/praisonaiagents/tools/arxiv_tools.py

@@ -0,0 +1,292 @@
+"""Tools for searching and retrieving papers from arXiv.
+
+Usage:
+from praisonaiagents.tools import arxiv_tools
+papers = arxiv_tools.search("quantum computing")
+paper = arxiv_tools.get_paper("2401.00123")
+
+or
+from praisonaiagents.tools import search_arxiv, get_arxiv_paper
+papers = search_arxiv("quantum computing")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import json
+
+# Map sort criteria to arxiv.SortCriterion
+SORT_CRITERIA = {
+    "relevance": "Relevance",
+    "lastupdateddate": "LastUpdatedDate",
+    "submitteddate": "SubmittedDate"
+}
+
+# Map sort order to arxiv.SortOrder
+SORT_ORDER = {
+    "ascending": "Ascending",
+    "descending": "Descending"
+}
+
+class ArxivTools:
+    """Tools for searching and retrieving papers from arXiv."""
+    
+    def __init__(self):
+        """Initialize ArxivTools and check for arxiv package."""
+        self._check_arxiv()
+        
+    def _check_arxiv(self):
+        """Check if arxiv package is installed."""
+        if util.find_spec("arxiv") is None:
+            raise ImportError("arxiv package is not available. Please install it using: pip install arxiv")
+        global arxiv
+        import arxiv
+
+    def search(
+        self,
+        query: str,
+        max_results: int = 10,
+        sort_by: str = "relevance",
+        sort_order: str = "descending",
+        include_fields: Optional[List[str]] = None
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Search arXiv for papers matching the query.
+        
+        Args:
+            query: Search query (e.g., "quantum computing", "author:Einstein")
+            max_results: Maximum number of results to return
+            sort_by: Sort results by ("relevance", "lastUpdatedDate", "submittedDate")
+            sort_order: Sort order ("ascending" or "descending")
+            include_fields: List of fields to include in results. If None, includes all.
+                Available fields: ["title", "authors", "summary", "comment", "journal_ref",
+                "doi", "primary_category", "categories", "links"]
+            
+        Returns:
+            List[Dict] or Dict: List of papers or error dict
+        """
+        try:
+            import arxiv
+            
+            # Configure search client
+            client = arxiv.Client()
+            
+            # Map sort criteria
+            sort_by_enum = getattr(arxiv.SortCriterion, SORT_CRITERIA[sort_by.lower()])
+            sort_order_enum = getattr(arxiv.SortOrder, SORT_ORDER[sort_order.lower()])
+            
+            # Build search query
+            search = arxiv.Search(
+                query=query,
+                max_results=max_results,
+                sort_by=sort_by_enum,
+                sort_order=sort_order_enum
+            )
+            
+            # Execute search
+            results = []
+            for result in client.results(search):
+                # Convert to dict with selected fields
+                paper = self._result_to_dict(result, include_fields)
+                results.append(paper)
+            
+            return results
+        except Exception as e:
+            error_msg = f"Error searching arXiv: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_paper(
+        self,
+        paper_id: str,
+        include_fields: Optional[List[str]] = None
+    ) -> Union[Dict[str, Any], Dict[str, str]]:
+        """
+        Get details of a specific paper by its arXiv ID.
+        
+        Args:
+            paper_id: arXiv paper ID (e.g., "2401.00123")
+            include_fields: List of fields to include in results. If None, includes all.
+                Available fields: ["title", "authors", "summary", "comment", "journal_ref",
+                "doi", "primary_category", "categories", "links"]
+            
+        Returns:
+            Dict: Paper details or error dict
+        """
+        try:
+            import arxiv
+            
+            # Configure client
+            client = arxiv.Client()
+            
+            # Get paper by ID
+            search = arxiv.Search(id_list=[paper_id])
+            results = list(client.results(search))
+            
+            if not results:
+                return {"error": f"Paper with ID {paper_id} not found"}
+            
+            # Convert to dict with selected fields
+            paper = self._result_to_dict(results[0], include_fields)
+            return paper
+        except Exception as e:
+            error_msg = f"Error getting paper {paper_id}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_papers_by_author(
+        self,
+        author: str,
+        max_results: int = 10,
+        sort_by: str = "submittedDate",
+        sort_order: str = "descending",
+        include_fields: Optional[List[str]] = None
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Get papers by a specific author.
+        
+        Args:
+            author: Author name (e.g., "Einstein")
+            max_results: Maximum number of results to return
+            sort_by: Sort results by ("relevance", "lastUpdatedDate", "submittedDate")
+            sort_order: Sort order ("ascending" or "descending")
+            include_fields: List of fields to include in results
+            
+        Returns:
+            List[Dict] or Dict: List of papers or error dict
+        """
+        query = f'au:"{author}"'
+        return self.search(query, max_results, sort_by, sort_order, include_fields)
+
+    def get_papers_by_category(
+        self,
+        category: str,
+        max_results: int = 10,
+        sort_by: str = "submittedDate",
+        sort_order: str = "descending",
+        include_fields: Optional[List[str]] = None
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Get papers from a specific category.
+        
+        Args:
+            category: arXiv category (e.g., "cs.AI", "physics.gen-ph")
+            max_results: Maximum number of results to return
+            sort_by: Sort results by ("relevance", "lastUpdatedDate", "submittedDate")
+            sort_order: Sort order ("ascending" or "descending")
+            include_fields: List of fields to include in results
+            
+        Returns:
+            List[Dict] or Dict: List of papers or error dict
+        """
+        query = f'cat:{category}'
+        return self.search(query, max_results, sort_by, sort_order, include_fields)
+
+    def _result_to_dict(
+        self,
+        result: Any,
+        include_fields: Optional[List[str]] = None
+    ) -> Dict[str, Any]:
+        """Convert arxiv.Result to dictionary with selected fields."""
+        # Default fields to include
+        if include_fields is None:
+            include_fields = [
+                "title", "authors", "summary", "comment", "journal_ref",
+                "doi", "primary_category", "categories", "links"
+            ]
+        
+        # Build paper dict with selected fields
+        paper = {}
+        
+        # Always include these basic fields
+        paper["arxiv_id"] = result.entry_id.split("/")[-1]
+        paper["updated"] = result.updated.isoformat() if result.updated else None
+        paper["published"] = result.published.isoformat() if result.published else None
+        
+        # Add requested fields
+        if "title" in include_fields:
+            paper["title"] = result.title
+        if "authors" in include_fields:
+            paper["authors"] = [str(author) for author in result.authors]
+        if "summary" in include_fields:
+            paper["summary"] = result.summary
+        if "comment" in include_fields:
+            paper["comment"] = result.comment
+        if "journal_ref" in include_fields:
+            paper["journal_ref"] = result.journal_ref
+        if "doi" in include_fields:
+            paper["doi"] = result.doi
+        if "primary_category" in include_fields:
+            paper["primary_category"] = result.primary_category
+        if "categories" in include_fields:
+            paper["categories"] = result.categories
+        if "links" in include_fields:
+            paper["pdf_url"] = result.pdf_url
+            paper["abstract_url"] = f"https://arxiv.org/abs/{paper['arxiv_id']}"
+        
+        return paper
+
+# Create instance for direct function access
+_arxiv_tools = ArxivTools()
+search_arxiv = _arxiv_tools.search
+get_arxiv_paper = _arxiv_tools.get_paper
+get_papers_by_author = _arxiv_tools.get_papers_by_author
+get_papers_by_category = _arxiv_tools.get_papers_by_category
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("ArxivTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Search for papers
+    print("1. Searching for Papers")
+    print("------------------------------")
+    query = "quantum computing"
+    papers = search_arxiv(query, max_results=3)
+    print(f"Papers about {query}:")
+    if isinstance(papers, list):
+        print(json.dumps(papers, indent=2))
+    else:
+        print(papers)  # Show error
+    print()
+    
+    # 2. Get specific paper
+    print("2. Getting Specific Paper")
+    print("------------------------------")
+    if isinstance(papers, list) and papers:
+        paper_id = papers[0]["arxiv_id"]
+        paper = get_arxiv_paper(paper_id)
+        print(f"Paper {paper_id}:")
+        if "error" not in paper:
+            print(json.dumps(paper, indent=2))
+        else:
+            print(paper)  # Show error
+    print()
+    
+    # 3. Get papers by author
+    print("3. Getting Papers by Author")
+    print("------------------------------")
+    author = "Yoshua Bengio"
+    author_papers = get_papers_by_author(author, max_results=3)
+    print(f"Papers by {author}:")
+    if isinstance(author_papers, list):
+        print(json.dumps(author_papers, indent=2))
+    else:
+        print(author_papers)  # Show error
+    print()
+    
+    # 4. Get papers by category
+    print("4. Getting Papers by Category")
+    print("------------------------------")
+    category = "cs.AI"
+    category_papers = get_papers_by_category(category, max_results=3)
+    print(f"Papers in category {category}:")
+    if isinstance(category_papers, list):
+        print(json.dumps(category_papers, indent=2))
+    else:
+        print(category_papers)  # Show error
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/calculator_tools.py

@@ -0,0 +1,278 @@
+"""Tools for performing calculations.
+
+Usage:
+from praisonaiagents.tools import calculator_tools
+result = calculator_tools.evaluate("2 + 2 * 3")
+
+or
+from praisonaiagents.tools import evaluate, solve_equation, convert_units
+result = evaluate("2 + 2 * 3")
+"""
+
+import logging
+from typing import List, Dict, Optional, Any
+from importlib import util
+import math
+
+class CalculatorTools:
+    """Tools for performing calculations."""
+    
+    def __init__(self):
+        """Initialize CalculatorTools."""
+        self._check_dependencies()
+        
+    def _check_dependencies(self):
+        """Check if required packages are installed."""
+        missing = []
+        for package in ['sympy', 'pint']:
+            if util.find_spec(package) is None:
+                missing.append(package)
+        
+        if missing:
+            raise ImportError(
+                f"Required packages not available. Please install: {', '.join(missing)}\n"
+                f"Run: pip install {' '.join(missing)}"
+            )
+
+    def evaluate(
+        self,
+        expression: str,
+        variables: Optional[Dict[str, float]] = None,
+        precision: int = 10
+    ) -> Dict[str, Any]:
+        """Evaluate a mathematical expression."""
+        try:
+            if util.find_spec('sympy') is None:
+                error_msg = "sympy package is not available. Please install it using: pip install sympy"
+                logging.error(error_msg)
+                return {"error": error_msg}
+
+            # Import sympy only when needed
+            import sympy
+
+            # Replace common mathematical functions
+            expression = expression.lower()
+            replacements = {
+                'sin(': 'math.sin(',
+                'cos(': 'math.cos(',
+                'tan(': 'math.tan(',
+                'sqrt(': 'math.sqrt(',
+                'log(': 'math.log(',
+                'exp(': 'math.exp(',
+                'pi': str(math.pi),
+                'e': str(math.e)
+            }
+            for old, new in replacements.items():
+                expression = expression.replace(old, new)
+            
+            # Create safe namespace
+            safe_dict = {
+                'math': math,
+                '__builtins__': None
+            }
+            if variables:
+                safe_dict.update(variables)
+            
+            # Evaluate expression
+            result = eval(expression, safe_dict)
+            
+            # Round to specified precision
+            if isinstance(result, (int, float)):
+                return {"result": round(float(result), precision)}
+            return {"error": "Invalid expression"}
+        except Exception as e:
+            error_msg = f"Error evaluating expression: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def solve_equation(
+        self,
+        equation: str,
+        variable: str = 'x'
+    ) -> Dict[str, Any]:
+        """Solve a mathematical equation."""
+        try:
+            if util.find_spec('sympy') is None:
+                error_msg = "sympy package is not available. Please install it using: pip install sympy"
+                logging.error(error_msg)
+                return {"error": error_msg}
+
+            # Import sympy only when needed
+            import sympy
+
+            # Parse equation
+            if '=' in equation:
+                left, right = equation.split('=')
+                equation = f"({left}) - ({right})"
+            
+            # Convert to SymPy expression
+            x = sympy.Symbol(variable)
+            expr = sympy.sympify(equation)
+            
+            # Solve equation
+            solutions = sympy.solve(expr, x)
+            
+            # Convert complex solutions to real if possible
+            real_solutions = []
+            for sol in solutions:
+                if sol.is_real:
+                    real_solutions.append(float(sol))
+            
+            return {"solutions": real_solutions} if real_solutions else {"error": "No real solutions found"}
+        except Exception as e:
+            error_msg = f"Error solving equation: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def convert_units(
+        self,
+        value: float,
+        from_unit: str,
+        to_unit: str
+    ) -> Dict[str, Any]:
+        """Convert between different units."""
+        try:
+            if util.find_spec('pint') is None:
+                error_msg = "pint package is not available. Please install it using: pip install pint"
+                logging.error(error_msg)
+                return {"error": error_msg}
+
+            # Import pint only when needed
+            import pint
+
+            # Create quantity with source unit
+            ureg = pint.UnitRegistry()
+            quantity = value * ureg(from_unit)
+            
+            # Convert to target unit
+            result = quantity.to(to_unit)
+            
+            return {"result": float(result.magnitude)}
+        except Exception as e:
+            error_msg = f"Error converting units: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def calculate_statistics(
+        self,
+        numbers: List[float]
+    ) -> Dict[str, Any]:
+        """Calculate basic statistics for a list of numbers."""
+        try:
+            if not numbers:
+                return {"error": "No numbers provided"}
+            
+            n = len(numbers)
+            mean = sum(numbers) / n
+            
+            # Calculate variance and std dev
+            squared_diff_sum = sum((x - mean) ** 2 for x in numbers)
+            variance = squared_diff_sum / (n - 1) if n > 1 else 0
+            std_dev = math.sqrt(variance)
+            
+            # Calculate median
+            sorted_nums = sorted(numbers)
+            if n % 2 == 0:
+                median = (sorted_nums[n//2 - 1] + sorted_nums[n//2]) / 2
+            else:
+                median = sorted_nums[n//2]
+            
+            return {
+                "mean": mean,
+                "median": median,
+                "std_dev": std_dev,
+                "variance": variance,
+                "min": min(numbers),
+                "max": max(numbers),
+                "range": max(numbers) - min(numbers),
+                "count": n
+            }
+        except Exception as e:
+            error_msg = f"Error calculating statistics: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def calculate_financial(
+        self,
+        principal: float,
+        rate: float,
+        time: float,
+        frequency: str = 'yearly'
+    ) -> Dict[str, Any]:
+        """Calculate financial metrics like compound interest."""
+        try:
+            # Convert rate to decimal
+            rate = rate / 100
+            
+            # Get compounding frequency
+            freq_map = {
+                'yearly': 1,
+                'semi-annual': 2,
+                'quarterly': 4,
+                'monthly': 12,
+                'daily': 365
+            }
+            n = freq_map.get(frequency.lower(), 1)
+            
+            # Calculate compound interest
+            amount = principal * (1 + rate/n)**(n*time)
+            interest = amount - principal
+            
+            # Calculate simple interest
+            simple_interest = principal * rate * time
+            
+            return {
+                "final_amount": amount,
+                "compound_interest": interest,
+                "simple_interest": simple_interest,
+                "difference": interest - simple_interest
+            }
+        except Exception as e:
+            error_msg = f"Error calculating financial metrics: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+# Create instance for direct function access
+_calculator_tools = CalculatorTools()
+evaluate = _calculator_tools.evaluate
+solve_equation = _calculator_tools.solve_equation
+convert_units = _calculator_tools.convert_units
+calculate_statistics = _calculator_tools.calculate_statistics
+calculate_financial = _calculator_tools.calculate_financial
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("CalculatorTools Demonstration")
+    print("==================================================\n")
+
+    print("1. Basic Arithmetic")
+    print("------------------------------")
+    print("2 + 2 =", evaluate("2 + 2")["result"])
+    print("5 * 6 =", evaluate("5 * 6")["result"])
+    print("10 / 3 =", evaluate("10 / 3")["result"])
+    print()
+
+    print("2. Complex Expressions")
+    print("------------------------------")
+    print("(2 + 3) * 4 =", evaluate("(2 + 3) * 4")["result"])
+    print("2^3 + 4 =", evaluate("2^3 + 4")["result"])
+    print("sqrt(16) + 2 =", evaluate("sqrt(16) + 2")["result"])
+    print()
+
+    print("3. Solving Equations")
+    print("------------------------------")
+    result = solve_equation("x^2 - 5*x + 6 = 0")
+    print("x^2 - 5*x + 6 = 0")
+    print("Solutions:", result["solutions"])
+    print()
+
+    print("4. Unit Conversions")
+    print("------------------------------")
+    print("100 meters to kilometers:", convert_units(100, "meters", "kilometers")["result"])
+    print("1 hour to minutes:", convert_units(1, "hours", "minutes")["result"])
+    print("32 fahrenheit to celsius:", convert_units(32, "fahrenheit", "celsius")["result"])
+    print()
+
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/csv_tools.py

@@ -0,0 +1,266 @@
+"""Tools for working with CSV files.
+
+Usage:
+from praisonaiagents.tools import csv_tools
+df = csv_tools.read_csv("data.csv")
+
+or
+from praisonaiagents.tools import read_csv, write_csv, merge_csv
+df = read_csv("data.csv")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any, TYPE_CHECKING
+from importlib import util
+import json
+import csv
+from pathlib import Path
+
+if TYPE_CHECKING:
+    import pandas as pd
+
+class CSVTools:
+    """Tools for working with CSV files."""
+    
+    def __init__(self):
+        """Initialize CSVTools."""
+        pass
+
+    def _get_pandas(self) -> Optional['pd']:
+        """Get pandas module, installing if needed"""
+        if util.find_spec('pandas') is None:
+            error_msg = "pandas package is not available. Please install it using: pip install pandas"
+            logging.error(error_msg)
+            return None
+        import pandas as pd
+        return pd
+
+    def read_csv(
+        self,
+        filepath: str,
+        encoding: str = 'utf-8',
+        delimiter: str = ',',
+        header: Union[int, List[int], None] = 0,
+        usecols: Optional[List[str]] = None,
+        dtype: Optional[Dict[str, str]] = None,
+        parse_dates: Optional[List[str]] = None,
+        na_values: Optional[List[str]] = None,
+        nrows: Optional[int] = None
+    ) -> List[Dict[str, Any]]:
+        """Read a CSV file with advanced options.
+        
+        Args:
+            filepath: Path to CSV file
+            encoding: File encoding
+            delimiter: Column delimiter
+            header: Row number(s) to use as column names
+            usecols: Columns to read
+            dtype: Dict of column dtypes
+            parse_dates: List of columns to parse as dates
+            na_values: Additional strings to recognize as NA/NaN
+            nrows: Number of rows to read
+            
+        Returns:
+            List of row dicts
+        """
+        try:
+            pd = self._get_pandas()
+            if pd is None:
+                return {"error": "pandas package not available"}
+
+            df = pd.read_csv(
+                filepath,
+                encoding=encoding,
+                delimiter=delimiter,
+                header=header,
+                usecols=usecols,
+                dtype=dtype,
+                parse_dates=parse_dates,
+                na_values=na_values,
+                nrows=nrows
+            )
+            return df.to_dict('records')
+            
+        except Exception as e:
+            error_msg = f"Error reading CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def write_csv(
+        self,
+        filepath: str,
+        data: List[Dict[str, Any]],
+        encoding: str = 'utf-8',
+        delimiter: str = ',',
+        index: bool = False,
+        header: bool = True,
+        float_format: Optional[str] = None,
+        date_format: Optional[str] = None,
+        mode: str = 'w'
+    ) -> bool:
+        """Write data to a CSV file.
+        
+        Args:
+            filepath: Path to CSV file
+            data: List of row dicts to write
+            encoding: File encoding
+            delimiter: Column delimiter
+            index: Whether to write row indices
+            header: Whether to write column headers
+            float_format: Format string for float values
+            date_format: Format string for date values
+            mode: Write mode ('w' for write, 'a' for append)
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            pd = self._get_pandas()
+            if pd is None:
+                return False
+
+            df = pd.DataFrame(data)
+            df.to_csv(
+                filepath,
+                encoding=encoding,
+                sep=delimiter,
+                index=index,
+                header=header,
+                float_format=float_format,
+                date_format=date_format,
+                mode=mode
+            )
+            return True
+            
+        except Exception as e:
+            error_msg = f"Error writing CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def merge_csv(
+        self,
+        files: List[str],
+        output_file: str,
+        how: str = 'inner',
+        on: Optional[Union[str, List[str]]] = None,
+        suffixes: Optional[tuple] = None
+    ) -> bool:
+        """Merge multiple CSV files.
+        
+        Args:
+            files: List of CSV files to merge
+            output_file: Output file path
+            how: Merge method ('inner', 'outer', 'left', 'right')
+            on: Column(s) to merge on
+            suffixes: Suffixes for overlapping columns
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            if len(files) < 2:
+                error_msg = "At least two files are required for merging"
+                logging.error(error_msg)
+                return False
+            
+            pd = self._get_pandas()
+            if pd is None:
+                return False
+
+            # Read first file
+            result = pd.read_csv(files[0])
+            
+            # Merge with remaining files
+            for file in files[1:]:
+                df = pd.read_csv(file)
+                result = pd.merge(
+                    result,
+                    df,
+                    how=how,
+                    on=on,
+                    suffixes=suffixes or ('_1', '_2')
+                )
+            
+            # Write merged result
+            result.to_csv(output_file, index=False)
+            return True
+            
+        except Exception as e:
+            error_msg = f"Error merging CSV files: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+# Create instance for direct function access
+_csv_tools = CSVTools()
+read_csv = _csv_tools.read_csv
+write_csv = _csv_tools.write_csv
+merge_csv = _csv_tools.merge_csv
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("CSVTools Demonstration")
+    print("==================================================\n")
+    
+    # Create a temporary file for testing
+    import tempfile
+    import os
+    
+    with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp:
+        temp_file = temp.name
+        
+        print("1. Writing CSV File")
+        print("------------------------------")
+        data = [
+            {"name": "Alice", "age": 25, "city": "New York"},
+            {"name": "Bob", "age": 30, "city": "San Francisco"},
+            {"name": "Charlie", "age": 35, "city": "Chicago"}
+        ]
+        result = write_csv(temp_file, data)
+        print(f"Data written successfully: {result}")
+        print()
+        
+        print("2. Reading CSV File")
+        print("------------------------------")
+        read_data = read_csv(temp_file)
+        print("Contents of CSV file:")
+        for row in read_data:
+            print(row)
+        print()
+        
+        print("3. Merging CSV Files")
+        print("------------------------------")
+        # Create a second file for merging
+        with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp2:
+            temp_file2 = temp2.name
+            data2 = [
+                {"name": "Alice", "salary": 75000},
+                {"name": "Bob", "salary": 85000},
+                {"name": "Charlie", "salary": 95000}
+            ]
+            write_csv(temp_file2, data2)
+            
+            # Merge files
+            with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp3:
+                temp_file3 = temp3.name
+                result = merge_csv(
+                    [temp_file, temp_file2],
+                    temp_file3,
+                    how='inner',
+                    on='name'
+                )
+                print(f"Files merged successfully: {result}")
+                if result:
+                    merged_data = read_csv(temp_file3)
+                    print("\nMerged contents:")
+                    for row in merged_data:
+                        print(row)
+                print()
+        
+        # Clean up temporary files
+        os.unlink(temp_file)
+        os.unlink(temp_file2)
+        os.unlink(temp_file3)
+    
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================\n")

agents/praisonaiagents/tools/duckdb_tools.py

@@ -0,0 +1,268 @@
+"""Tools for working with DuckDB databases.
+
+Usage:
+from praisonaiagents.tools import duckdb_tools
+df = duckdb_tools.query("SELECT * FROM my_table")
+
+or
+from praisonaiagents.tools import query_db, create_table, load_data
+df = query_db("SELECT * FROM my_table")
+"""
+
+import logging
+from typing import List, Dict, Any, Optional, Union, TYPE_CHECKING
+from importlib import util
+import json
+
+if TYPE_CHECKING:
+    import duckdb
+    import pandas as pd
+
+class DuckDBTools:
+    """Tools for working with DuckDB databases."""
+    
+    def __init__(self, database: str = ':memory:'):
+        """Initialize DuckDBTools.
+        
+        Args:
+            database: Path to database file or ':memory:' for in-memory database
+        """
+        self.database = database
+        self._conn = None
+
+    def _get_duckdb(self) -> Optional['duckdb']:
+        """Get duckdb module, installing if needed"""
+        if util.find_spec('duckdb') is None:
+            error_msg = "duckdb package is not available. Please install it using: pip install duckdb"
+            logging.error(error_msg)
+            return None
+        import duckdb
+        return duckdb
+
+    def _get_pandas(self) -> Optional['pd']:
+        """Get pandas module, installing if needed"""
+        if util.find_spec('pandas') is None:
+            error_msg = "pandas package is not available. Please install it using: pip install pandas"
+            logging.error(error_msg)
+            return None
+        import pandas as pd
+        return pd
+
+    def _get_connection(self) -> Optional['duckdb.DuckDBPyConnection']:
+        """Get or create database connection"""
+        if self._conn is None:
+            duckdb = self._get_duckdb()
+            if duckdb is None:
+                return None
+            try:
+                self._conn = duckdb.connect(self.database)
+            except Exception as e:
+                error_msg = f"Error connecting to database {self.database}: {str(e)}"
+                logging.error(error_msg)
+                return None
+        return self._conn
+
+    def execute_query(
+        self,
+        query: str,
+        params: Optional[Union[tuple, dict]] = None,
+        return_df: bool = True
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """Execute a SQL query.
+        
+        Args:
+            query: SQL query to execute
+            params: Query parameters
+            return_df: If True, return results as DataFrame records
+            
+        Returns:
+            Query results as list of dicts, or error dict
+        """
+        try:
+            conn = self._get_connection()
+            if conn is None:
+                return {"error": "Could not connect to database"}
+
+            if params:
+                result = conn.execute(query, params)
+            else:
+                result = conn.execute(query)
+
+            if return_df:
+                pd = self._get_pandas()
+                if pd is None:
+                    return {"error": "pandas package not available"}
+                df = result.df()
+                return df.to_dict('records')
+            else:
+                return [dict(row) for row in result.fetchall()]
+
+        except Exception as e:
+            error_msg = f"Error executing query: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def load_csv(
+        self,
+        table_name: str,
+        filepath: str,
+        schema: Optional[Dict[str, str]] = None,
+        if_exists: str = 'replace'
+    ) -> bool:
+        """Load a CSV file into a table.
+        
+        Args:
+            table_name: Name of table to create
+            filepath: Path to CSV file
+            schema: Optional column definitions
+            if_exists: What to do if table exists ('fail', 'replace', 'append')
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            conn = self._get_connection()
+            if conn is None:
+                return False
+
+            # Check if table exists
+            exists = conn.execute(f"""
+                SELECT name FROM sqlite_master 
+                WHERE type='table' AND name='{table_name}'
+            """).fetchone() is not None
+
+            if exists:
+                if if_exists == 'fail':
+                    raise ValueError(f"Table {table_name} already exists")
+                elif if_exists == 'replace':
+                    conn.execute(f"DROP TABLE IF EXISTS {table_name}")
+                elif if_exists != 'append':
+                    raise ValueError("if_exists must be 'fail', 'replace', or 'append'")
+
+            # Create table if needed
+            if not exists or if_exists == 'replace':
+                if schema:
+                    # Create table with schema
+                    columns = ', '.join(f"{k} {v}" for k, v in schema.items())
+                    conn.execute(f"CREATE TABLE {table_name} ({columns})")
+                else:
+                    # Infer schema from CSV
+                    conn.execute(f"""
+                        CREATE TABLE {table_name} AS 
+                        SELECT * FROM read_csv_auto('{filepath}')
+                        WHERE 1=0
+                    """)
+
+            # Load data
+            conn.execute(f"""
+                INSERT INTO {table_name}
+                SELECT * FROM read_csv_auto('{filepath}')
+            """)
+
+            return True
+
+        except Exception as e:
+            error_msg = f"Error loading CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def export_csv(
+        self,
+        query: str,
+        filepath: str,
+        params: Optional[Union[tuple, dict]] = None
+    ) -> bool:
+        """Export query results to CSV.
+        
+        Args:
+            query: SQL query to execute
+            filepath: Output file path
+            params: Optional query parameters
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            # Execute query and get results as DataFrame
+            results = self.execute_query(query, params)
+            if isinstance(results, dict) and 'error' in results:
+                return False
+
+            pd = self._get_pandas()
+            if pd is None:
+                return False
+
+            # Convert to DataFrame and save
+            df = pd.DataFrame(results)
+            df.to_csv(filepath, index=False)
+            return True
+
+        except Exception as e:
+            error_msg = f"Error exporting to CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def close(self):
+        """Close database connection."""
+        if self._conn:
+            self._conn.close()
+            self._conn = None
+
+# Create instance for direct function access
+_duckdb_tools = DuckDBTools()
+execute_query = _duckdb_tools.execute_query
+load_csv = _duckdb_tools.load_csv
+export_csv = _duckdb_tools.export_csv
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("DuckDBTools Demonstration")
+    print("==================================================\n")
+    
+    # Create a temporary file for testing
+    import tempfile
+    import os
+    
+    with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp:
+        temp_file = temp.name
+        
+        # Create sample data
+        with open(temp_file, 'w') as f:
+            f.write("name,age,city\n")
+            f.write("Alice,25,New York\n")
+            f.write("Bob,30,San Francisco\n")
+            f.write("Charlie,35,Chicago\n")
+        
+        print("1. Loading CSV File")
+        print("------------------------------")
+        result = load_csv('users', temp_file)
+        print(f"CSV loaded successfully: {result}")
+        print()
+        
+        print("2. Executing Query")
+        print("------------------------------")
+        query = "SELECT * FROM users WHERE age > 25"
+        results = execute_query(query)
+        print("Query results:")
+        for row in results:
+            print(row)
+        print()
+        
+        print("3. Exporting Query Results")
+        print("------------------------------")
+        with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as temp2:
+            temp_file2 = temp2.name
+            result = export_csv(query, temp_file2)
+            print(f"Results exported successfully: {result}")
+            if result:
+                print("\nExported file contents:")
+                with open(temp_file2) as f:
+                    print(f.read())
+        
+        # Clean up temporary files
+        os.unlink(temp_file)
+        os.unlink(temp_file2)
+    
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================\n")

agents/praisonaiagents/tools/duckduckgo_tools.py

@@ -0,0 +1,52 @@
+"""DuckDuckGo search functionality.
+
+Usage:
+from praisonaiagents.tools import internet_search
+results = internet_search("AI news")
+
+or 
+from praisonaiagents.tools import duckduckgo
+results = duckduckgo("AI news")
+"""
+
+from typing import List, Dict
+import logging
+from importlib import util
+
+def internet_search(query: str) -> List[Dict]:
+    """Perform an internet search using DuckDuckGo."""
+    # Check if duckduckgo_search is installed
+    if util.find_spec("duckduckgo_search") is None:
+        error_msg = "DuckDuckGo search is not available. Please install duckduckgo_search package using: pip install duckduckgo_search"
+        logging.error(error_msg)
+        return [{"error": error_msg}]
+
+    try:
+        # Import only when needed
+        from duckduckgo_search import DDGS
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=5):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+
+    except Exception as e:
+        error_msg = f"Error during DuckDuckGo search: {e}"
+        logging.error(error_msg)
+        return [{"error": error_msg}]
+
+def duckduckgo(query: str) -> List[Dict]:
+    """Alias for internet_search function."""
+    return internet_search(query)
+
+if __name__ == "__main__":
+    # Example usage
+    results = internet_search("Python programming")
+    for result in results:
+        print(f"\nTitle: {result.get('title')}")
+        print(f"URL: {result.get('url')}")
+        print(f"Snippet: {result.get('snippet')}")

agents/praisonaiagents/tools/excel_tools.py

@@ -0,0 +1,310 @@
+"""Tools for working with Excel files.
+
+Usage:
+from praisonaiagents.tools import excel_tools
+df = excel_tools.read_excel("data.xlsx")
+
+or
+from praisonaiagents.tools import read_excel, write_excel, merge_excel
+df = read_excel("data.xlsx")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any, TYPE_CHECKING, Tuple
+from importlib import util
+import json
+from pathlib import Path
+import tempfile
+import os
+
+if TYPE_CHECKING:
+    import pandas as pd
+    from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
+    from openpyxl.chart import BarChart, LineChart, PieChart, Reference
+    from openpyxl.chart.label import DataLabelList
+
+class ExcelTools:
+    """Tools for working with Excel files."""
+    
+    def __init__(self):
+        """Initialize ExcelTools."""
+        pass
+
+    def _get_pandas(self) -> Optional['pd']:
+        """Get pandas module, installing if needed"""
+        if util.find_spec('pandas') is None:
+            error_msg = "pandas package is not available. Please install it using: pip install pandas"
+            logging.error(error_msg)
+            return None
+        if util.find_spec('openpyxl') is None:
+            error_msg = "openpyxl package is not available. Please install it using: pip install openpyxl"
+            logging.error(error_msg)
+            return None
+        import pandas as pd
+        return pd
+
+    def read_excel(
+        self,
+        filepath: str,
+        sheet_name: Optional[Union[str, int, List[Union[str, int]]]] = 0,
+        header: Optional[int] = 0,
+        usecols: Optional[List[str]] = None,
+        skiprows: Optional[Union[int, List[int]]] = None,
+        na_values: Optional[List[str]] = None,
+        dtype: Optional[Dict[str, str]] = None
+    ) -> Union[Dict[str, List[Dict[str, Any]]], List[Dict[str, Any]]]:
+        """Read an Excel file with advanced options.
+        
+        Args:
+            filepath: Path to Excel file
+            sheet_name: Sheet name(s) or index(es)
+            header: Row number(s) to use as column names
+            usecols: Columns to read
+            skiprows: Line numbers to skip
+            na_values: Additional strings to recognize as NA/NaN
+            dtype: Dict of column dtypes
+            
+        Returns:
+            Dict of sheet names to data if multiple sheets, else list of row dicts
+        """
+        try:
+            pd = self._get_pandas()
+            if pd is None:
+                return {"error": "Required packages not available"}
+
+            # Read Excel file
+            df = pd.read_excel(
+                filepath,
+                sheet_name=sheet_name,
+                header=header,
+                usecols=usecols,
+                skiprows=skiprows,
+                na_values=na_values,
+                dtype=dtype,
+                engine='openpyxl'
+            )
+            
+            # Convert to dict format
+            if isinstance(df, dict):
+                return {
+                    name: df[name].to_dict('records')
+                    for name in df.keys()
+                }
+            else:
+                return df.to_dict('records')
+                
+        except Exception as e:
+            error_msg = f"Error reading Excel file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def write_excel(
+        self,
+        filepath: str,
+        data: Union[Dict[str, List[Dict[str, Any]]], List[Dict[str, Any]]],
+        sheet_name: Optional[str] = None,
+        index: bool = False,
+        header: bool = True,
+        mode: str = 'w'
+    ) -> bool:
+        """Write data to an Excel file.
+        
+        Args:
+            filepath: Path to Excel file
+            data: Data to write (dict of sheet names to data or list of row dicts)
+            sheet_name: Sheet name if data is a list
+            index: Whether to write row indices
+            header: Whether to write column headers
+            mode: Write mode ('w' for write, 'a' for append)
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            pd = self._get_pandas()
+            if pd is None:
+                return False
+
+            # Convert data to DataFrame(s)
+            if isinstance(data, dict):
+                if mode == 'a' and os.path.exists(filepath):
+                    book = pd.ExcelFile(filepath)
+                    with pd.ExcelWriter(filepath, engine='openpyxl') as writer:
+                        # Copy existing sheets
+                        for sheet in book.sheet_names:
+                            pd.read_excel(filepath, sheet_name=sheet).to_excel(
+                                writer, sheet_name=sheet, index=index, header=header
+                            )
+                        # Add new sheets
+                        for name, sheet_data in data.items():
+                            df = pd.DataFrame(sheet_data)
+                            df.to_excel(
+                                writer, sheet_name=name, index=index, header=header
+                            )
+                else:
+                    with pd.ExcelWriter(filepath, engine='openpyxl') as writer:
+                        for name, sheet_data in data.items():
+                            df = pd.DataFrame(sheet_data)
+                            df.to_excel(
+                                writer, sheet_name=name, index=index, header=header
+                            )
+            else:
+                if mode == 'a' and os.path.exists(filepath):
+                    book = pd.ExcelFile(filepath)
+                    with pd.ExcelWriter(filepath, engine='openpyxl') as writer:
+                        # Copy existing sheets
+                        for sheet in book.sheet_names:
+                            pd.read_excel(filepath, sheet_name=sheet).to_excel(
+                                writer, sheet_name=sheet, index=index, header=header
+                            )
+                        # Add new sheet
+                        df = pd.DataFrame(data)
+                        df.to_excel(
+                            writer,
+                            sheet_name=sheet_name or 'Sheet1',
+                            index=index,
+                            header=header
+                        )
+                else:
+                    df = pd.DataFrame(data)
+                    df.to_excel(
+                        filepath,
+                        sheet_name=sheet_name or 'Sheet1',
+                        index=index,
+                        header=header,
+                        engine='openpyxl'
+                    )
+            
+            return True
+            
+        except Exception as e:
+            error_msg = f"Error writing Excel file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def merge_excel(
+        self,
+        files: List[str],
+        output_file: str,
+        how: str = 'inner',
+        on: Optional[Union[str, List[str]]] = None,
+        suffixes: Optional[Tuple[str, str]] = None
+    ) -> bool:
+        """Merge multiple Excel files.
+        
+        Args:
+            files: List of Excel files to merge
+            output_file: Output file path
+            how: Merge method ('inner', 'outer', 'left', 'right')
+            on: Column(s) to merge on
+            suffixes: Suffixes for overlapping columns
+            
+        Returns:
+            bool: Success status
+        """
+        try:
+            if len(files) < 2:
+                error_msg = "At least two files are required for merging"
+                logging.error(error_msg)
+                return False
+            
+            # Read first file
+            result = self.read_excel(files[0])
+            
+            # Merge with remaining files
+            for file in files[1:]:
+                df = self.read_excel(file)
+                if isinstance(df, dict):
+                    df = next(iter(df.values()))
+                
+                pd = self._get_pandas()
+                if pd is None:
+                    return False
+                
+                result = pd.merge(
+                    pd.DataFrame(result),
+                    pd.DataFrame(df),
+                    how=how,
+                    on=on,
+                    suffixes=suffixes or ('_1', '_2')
+                ).to_dict('records')
+            
+            # Write merged result
+            return self.write_excel(output_file, result)
+            
+        except Exception as e:
+            error_msg = f"Error merging Excel files: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+# Create instance for direct function access
+_excel_tools = ExcelTools()
+read_excel = _excel_tools.read_excel
+write_excel = _excel_tools.write_excel
+merge_excel = _excel_tools.merge_excel
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("ExcelTools Demonstration")
+    print("==================================================\n")
+    
+    # Create a temporary file for testing
+    with tempfile.NamedTemporaryFile(suffix='.xlsx', delete=False) as temp:
+        temp_file = temp.name
+        
+        print("1. Writing Excel File")
+        print("------------------------------")
+        data = [
+            {"name": "Alice", "age": 25, "city": "New York"},
+            {"name": "Bob", "age": 30, "city": "San Francisco"},
+            {"name": "Charlie", "age": 35, "city": "Chicago"}
+        ]
+        result = write_excel(temp_file, data, "People")
+        print(f"Data written successfully: {result}")
+        print()
+        
+        print("2. Reading Excel File")
+        print("------------------------------")
+        read_data = read_excel(temp_file)
+        print("Contents of Excel file:")
+        for row in read_data:
+            print(row)
+        print()
+        
+        print("3. Merging Excel Files")
+        print("------------------------------")
+        # Create a second file for merging
+        with tempfile.NamedTemporaryFile(suffix='.xlsx', delete=False) as temp2:
+            temp_file2 = temp2.name
+            data2 = [
+                {"name": "Alice", "salary": 75000},
+                {"name": "Bob", "salary": 85000},
+                {"name": "Charlie", "salary": 95000}
+            ]
+            write_excel(temp_file2, data2, "Salaries")
+            
+            # Merge files
+            with tempfile.NamedTemporaryFile(suffix='.xlsx', delete=False) as temp3:
+                temp_file3 = temp3.name
+                result = merge_excel(
+                    [temp_file, temp_file2],
+                    temp_file3,
+                    how='inner',
+                    on='name'
+                )
+                print(f"Files merged successfully: {result}")
+                if result:
+                    merged_data = read_excel(temp_file3)
+                    print("\nMerged contents:")
+                    for row in merged_data:
+                        print(row)
+                print()
+        
+        # Clean up temporary files
+        os.unlink(temp_file)
+        os.unlink(temp_file2)
+        os.unlink(temp_file3)
+    
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================\n")

agents/praisonaiagents/tools/file_tools.py

@@ -0,0 +1,274 @@
+"""File handling tools for basic file operations.
+
+Usage:
+from praisonaiagents.tools import file_tools
+content = file_tools.read_file("example.txt")
+file_tools.write_file("output.txt", "Hello World")
+
+or 
+from praisonaiagents.tools import read_file, write_file, list_files
+content = read_file("example.txt")
+"""
+
+import os
+import json
+from typing import List, Dict, Union, Optional
+from pathlib import Path
+import shutil
+import logging
+
+class FileTools:
+    """Tools for file operations including read, write, list, and information."""
+    
+    @staticmethod
+    def read_file(filepath: str, encoding: str = 'utf-8') -> str:
+        """
+        Read content from a file.
+        
+        Args:
+            filepath: Path to the file
+            encoding: File encoding (default: utf-8)
+            
+        Returns:
+            str: Content of the file
+        """
+        try:
+            with open(filepath, 'r', encoding=encoding) as f:
+                return f.read()
+        except Exception as e:
+            error_msg = f"Error reading file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return error_msg
+
+    @staticmethod
+    def write_file(filepath: str, content: str, encoding: str = 'utf-8') -> bool:
+        """
+        Write content to a file.
+        
+        Args:
+            filepath: Path to the file
+            content: Content to write
+            encoding: File encoding (default: utf-8)
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            # Create directory if it doesn't exist
+            os.makedirs(os.path.dirname(filepath), exist_ok=True)
+            with open(filepath, 'w', encoding=encoding) as f:
+                f.write(content)
+            return True
+        except Exception as e:
+            error_msg = f"Error writing to file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    @staticmethod
+    def list_files(directory: str, pattern: Optional[str] = None) -> List[Dict[str, Union[str, int]]]:
+        """
+        List files in a directory with optional pattern matching.
+        
+        Args:
+            directory: Directory path
+            pattern: Optional glob pattern (e.g., "*.txt")
+            
+        Returns:
+            List[Dict]: List of file information dictionaries
+        """
+        try:
+            path = Path(directory)
+            if pattern:
+                files = path.glob(pattern)
+            else:
+                files = path.iterdir()
+
+            result = []
+            for file in files:
+                if file.is_file():
+                    stat = file.stat()
+                    result.append({
+                        'name': file.name,
+                        'path': str(file),
+                        'size': stat.st_size,
+                        'modified': stat.st_mtime,
+                        'created': stat.st_ctime
+                    })
+            return result
+        except Exception as e:
+            error_msg = f"Error listing files in {directory}: {str(e)}"
+            logging.error(error_msg)
+            return [{'error': error_msg}]
+
+    @staticmethod
+    def get_file_info(filepath: str) -> Dict[str, Union[str, int]]:
+        """
+        Get detailed information about a file.
+        
+        Args:
+            filepath: Path to the file
+            
+        Returns:
+            Dict: File information including size, dates, etc.
+        """
+        try:
+            path = Path(filepath)
+            if not path.exists():
+                return {'error': f'File not found: {filepath}'}
+            
+            stat = path.stat()
+            return {
+                'name': path.name,
+                'path': str(path),
+                'size': stat.st_size,
+                'modified': stat.st_mtime,
+                'created': stat.st_ctime,
+                'is_file': path.is_file(),
+                'is_dir': path.is_dir(),
+                'extension': path.suffix,
+                'parent': str(path.parent)
+            }
+        except Exception as e:
+            error_msg = f"Error getting file info for {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {'error': error_msg}
+
+    @staticmethod
+    def copy_file(src: str, dst: str) -> bool:
+        """
+        Copy a file from source to destination.
+        
+        Args:
+            src: Source file path
+            dst: Destination file path
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            # Create destination directory if it doesn't exist
+            os.makedirs(os.path.dirname(dst), exist_ok=True)
+            shutil.copy2(src, dst)
+            return True
+        except Exception as e:
+            error_msg = f"Error copying file from {src} to {dst}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    @staticmethod
+    def move_file(src: str, dst: str) -> bool:
+        """
+        Move a file from source to destination.
+        
+        Args:
+            src: Source file path
+            dst: Destination file path
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            # Create destination directory if it doesn't exist
+            os.makedirs(os.path.dirname(dst), exist_ok=True)
+            shutil.move(src, dst)
+            return True
+        except Exception as e:
+            error_msg = f"Error moving file from {src} to {dst}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    @staticmethod
+    def delete_file(filepath: str) -> bool:
+        """
+        Delete a file.
+        
+        Args:
+            filepath: Path to the file
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            os.remove(filepath)
+            return True
+        except Exception as e:
+            error_msg = f"Error deleting file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+# Create instance for direct function access
+_file_tools = FileTools()
+read_file = _file_tools.read_file
+write_file = _file_tools.write_file
+list_files = _file_tools.list_files
+get_file_info = _file_tools.get_file_info
+copy_file = _file_tools.copy_file
+move_file = _file_tools.move_file
+delete_file = _file_tools.delete_file
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("FileTools Demonstration")
+    print("==================================================\n")
+
+    # Create a test directory
+    test_dir = os.path.join(os.getcwd(), "test_files")
+    os.makedirs(test_dir, exist_ok=True)
+    
+    # Create test files
+    test_file = os.path.join(test_dir, "test_file.txt")
+    test_content = "Hello, this is a test file!"
+    
+    print("1. Writing to file")
+    print("------------------------------")
+    success = write_file(test_file, test_content)
+    print(f"Write successful: {success}\n")
+    
+    print("2. Reading from file")
+    print("------------------------------")
+    content = read_file(test_file)
+    print(f"Content: {content}\n")
+    
+    print("3. File Information")
+    print("------------------------------")
+    info = get_file_info(test_file)
+    print(json.dumps(info, indent=2))
+    print()
+    
+    print("4. Listing Files")
+    print("------------------------------")
+    files = list_files(test_dir, "*.txt")
+    for file in files:
+        print(f"Found: {file['name']} ({file['size']} bytes)")
+    print()
+    
+    print("5. Copying File")
+    print("------------------------------")
+    copy_file_path = os.path.join(test_dir, "test_file_copy.txt")
+    copy_success = copy_file(test_file, copy_file_path)
+    print(f"Copy successful: {copy_success}\n")
+    
+    print("6. Moving File")
+    print("------------------------------")
+    move_file_path = os.path.join(test_dir, "test_file_moved.txt")
+    move_success = move_file(copy_file_path, move_file_path)
+    print(f"Move successful: {move_success}\n")
+    
+    print("7. Deleting Files")
+    print("------------------------------")
+    delete_success = delete_file(test_file)
+    print(f"Delete original successful: {delete_success}")
+    delete_success = delete_file(move_file_path)
+    print(f"Delete moved file successful: {delete_success}\n")
+    
+    # Clean up test directory
+    try:
+        shutil.rmtree(test_dir)
+        print("Test directory cleaned up successfully")
+    except Exception as e:
+        print(f"Error cleaning up test directory: {str(e)}")
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/json_tools.py

@@ -0,0 +1,515 @@
+"""Tools for working with JSON files.
+
+Usage:
+from praisonaiagents.tools import json_tools
+data = json_tools.read_json("data.json")
+
+or
+from praisonaiagents.tools import read_json, write_json, merge_json
+data = read_json("data.json")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any, Tuple
+from importlib import util
+import json
+from datetime import datetime
+
+class JSONTools:
+    """Tools for working with JSON files."""
+    
+    def __init__(self):
+        """Initialize JSONTools."""
+        pass
+
+    def read_json(
+        self,
+        filepath: str,
+        encoding: str = 'utf-8',
+        validate_schema: Optional[Dict[str, Any]] = None
+    ) -> Dict[str, Any]:
+        """Read a JSON file with optional schema validation.
+        
+        Args:
+            filepath: Path to JSON file
+            encoding: File encoding
+            validate_schema: Optional JSON schema for validation
+            
+        Returns:
+            Dict with JSON data
+        """
+        try:
+            # Read JSON file
+            with open(filepath, 'r', encoding=encoding) as f:
+                data = json.load(f)
+            
+            # Validate against schema if provided
+            if validate_schema:
+                if util.find_spec('jsonschema') is None:
+                    error_msg = "jsonschema package is not available. Please install it using: pip install jsonschema"
+                    logging.error(error_msg)
+                    return {"error": error_msg}
+                import jsonschema
+                try:
+                    jsonschema.validate(instance=data, schema=validate_schema)
+                except jsonschema.exceptions.ValidationError as e:
+                    error_msg = f"JSON validation failed: {str(e)}"
+                    logging.error(error_msg)
+                    return {"error": error_msg}
+            
+            return data
+            
+        except Exception as e:
+            error_msg = f"Error reading JSON file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def write_json(
+        self,
+        data: Union[Dict[str, Any], List[Any]],
+        filepath: str,
+        encoding: str = 'utf-8',
+        indent: int = 2,
+        sort_keys: bool = False,
+        ensure_ascii: bool = False
+    ) -> bool:
+        """Write data to a JSON file.
+        
+        Args:
+            data: Data to write
+            filepath: Output file path
+            encoding: File encoding
+            indent: Number of spaces for indentation
+            sort_keys: Sort dictionary keys
+            ensure_ascii: Escape non-ASCII characters
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            with open(filepath, 'w', encoding=encoding) as f:
+                json.dump(
+                    data,
+                    f,
+                    indent=indent,
+                    sort_keys=sort_keys,
+                    ensure_ascii=ensure_ascii
+                )
+            return True
+        except Exception as e:
+            error_msg = f"Error writing JSON file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def merge_json(
+        self,
+        files: List[str],
+        output_file: str,
+        merge_arrays: bool = True,
+        overwrite_duplicates: bool = True
+    ) -> bool:
+        """Merge multiple JSON files.
+        
+        Args:
+            files: List of JSON files to merge
+            output_file: Output file path
+            merge_arrays: Merge arrays instead of overwriting
+            overwrite_duplicates: Overwrite duplicate keys
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            if len(files) < 2:
+                raise ValueError("At least two files are required for merging")
+            
+            # Read first file
+            result = self.read_json(files[0])
+            
+            # Merge with remaining files
+            for file in files[1:]:
+                data = self.read_json(file)
+                result = self._deep_merge(
+                    result,
+                    data,
+                    merge_arrays=merge_arrays,
+                    overwrite_duplicates=overwrite_duplicates
+                )
+            
+            # Write merged result
+            return self.write_json(result, output_file)
+        except Exception as e:
+            error_msg = f"Error merging JSON files: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def _deep_merge(
+        self,
+        dict1: Dict[str, Any],
+        dict2: Dict[str, Any],
+        merge_arrays: bool = True,
+        overwrite_duplicates: bool = True
+    ) -> Dict[str, Any]:
+        """Deep merge two dictionaries.
+        
+        Args:
+            dict1: First dictionary
+            dict2: Second dictionary
+            merge_arrays: Merge arrays instead of overwriting
+            overwrite_duplicates: Overwrite duplicate keys
+            
+        Returns:
+            Merged dictionary
+        """
+        result = dict1.copy()
+        
+        for key, value in dict2.items():
+            if key in result:
+                if isinstance(result[key], dict) and isinstance(value, dict):
+                    result[key] = self._deep_merge(
+                        result[key],
+                        value,
+                        merge_arrays=merge_arrays,
+                        overwrite_duplicates=overwrite_duplicates
+                    )
+                elif isinstance(result[key], list) and isinstance(value, list):
+                    if merge_arrays:
+                        result[key].extend(value)
+                    elif overwrite_duplicates:
+                        result[key] = value
+                elif overwrite_duplicates:
+                    result[key] = value
+            else:
+                result[key] = value
+        
+        return result
+
+    def validate_json(
+        self,
+        data: Union[Dict[str, Any], str],
+        schema: Dict[str, Any]
+    ) -> Tuple[bool, Optional[str]]:
+        """Validate JSON data against a schema.
+        
+        Args:
+            data: JSON data or filepath
+            schema: JSON schema for validation
+            
+        Returns:
+            Tuple of (is_valid, error_message)
+        """
+        try:
+            if util.find_spec('jsonschema') is None:
+                error_msg = "jsonschema package is not available. Please install it using: pip install jsonschema"
+                logging.error(error_msg)
+                return False, error_msg
+            import jsonschema
+
+            # Load data if filepath provided
+            if isinstance(data, str):
+                with open(data, 'r') as f:
+                    data = json.load(f)
+            
+            jsonschema.validate(instance=data, schema=schema)
+            return True, None
+            
+        except jsonschema.exceptions.ValidationError as e:
+            error_msg = f"JSON validation failed: {str(e)}"
+            logging.error(error_msg)
+            return False, error_msg
+            
+        except Exception as e:
+            error_msg = f"Error validating JSON: {str(e)}"
+            logging.error(error_msg)
+            return False, error_msg
+
+    def analyze_json(
+        self,
+        data: Union[Dict[str, Any], str],
+        max_depth: int = 10
+    ) -> Dict[str, Any]:
+        """Analyze JSON data structure.
+        
+        Args:
+            data: JSON data or filepath
+            max_depth: Maximum depth to analyze
+            
+        Returns:
+            Dict with analysis results
+        """
+        try:
+            # Load data if filepath provided
+            if isinstance(data, str):
+                data = self.read_json(data)
+            
+            def analyze_value(value: Any, depth: int = 0) -> Dict[str, Any]:
+                if depth >= max_depth:
+                    return {'type': 'max_depth_reached'}
+                
+                result = {'type': type(value).__name__}
+                
+                if isinstance(value, dict):
+                    result['size'] = len(value)
+                    result['keys'] = list(value.keys())
+                    if depth < max_depth - 1:
+                        result['children'] = {
+                            k: analyze_value(v, depth + 1)
+                            for k, v in value.items()
+                        }
+                
+                elif isinstance(value, list):
+                    result['length'] = len(value)
+                    if value:
+                        result['element_types'] = list(set(
+                            type(x).__name__ for x in value
+                        ))
+                        if depth < max_depth - 1:
+                            result['sample_elements'] = [
+                                analyze_value(x, depth + 1)
+                                for x in value[:5]
+                            ]
+                
+                elif isinstance(value, (int, float)):
+                    result.update({
+                        'value': value,
+                        'is_integer': isinstance(value, int)
+                    })
+                
+                elif isinstance(value, str):
+                    result.update({
+                        'length': len(value),
+                        'sample': value[:100] if len(value) > 100 else value
+                    })
+                
+                return result
+            
+            return {
+                'analysis_time': datetime.now().isoformat(),
+                'structure': analyze_value(data)
+            }
+        except Exception as e:
+            error_msg = f"Error analyzing JSON: {str(e)}"
+            logging.error(error_msg)
+            return {}
+
+    def transform_json(
+        self,
+        data: Union[Dict[str, Any], str],
+        transformations: List[Dict[str, Any]]
+    ) -> Dict[str, Any]:
+        """Transform JSON data using a list of operations.
+        
+        Args:
+            data: JSON data or filepath
+            transformations: List of transformation operations
+            
+        Returns:
+            Transformed JSON data
+        """
+        try:
+            # Load data if filepath provided
+            if isinstance(data, str):
+                data = self.read_json(data)
+            
+            result = data.copy()
+            
+            for transform in transformations:
+                op = transform.get('operation')
+                path = transform.get('path', '').split('.')
+                value = transform.get('value')
+                
+                if op == 'set':
+                    self._set_value(result, path, value)
+                elif op == 'delete':
+                    self._delete_value(result, path)
+                elif op == 'rename':
+                    old_path = path
+                    new_path = value.split('.')
+                    self._rename_key(result, old_path, new_path)
+                elif op == 'move':
+                    old_path = path
+                    new_path = value.split('.')
+                    self._move_value(result, old_path, new_path)
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error transforming JSON: {str(e)}"
+            logging.error(error_msg)
+            return data
+
+    def _set_value(self, data: Dict[str, Any], path: List[str], value: Any):
+        """Set a value at the specified path."""
+        current = data
+        for key in path[:-1]:
+            if key not in current:
+                current[key] = {}
+            current = current[key]
+        current[path[-1]] = value
+
+    def _delete_value(self, data: Dict[str, Any], path: List[str]):
+        """Delete a value at the specified path."""
+        current = data
+        for key in path[:-1]:
+            if key not in current:
+                return
+            current = current[key]
+        if path[-1] in current:
+            del current[path[-1]]
+
+    def _rename_key(
+        self,
+        data: Dict[str, Any],
+        old_path: List[str],
+        new_path: List[str]
+    ):
+        """Rename a key at the specified path."""
+        value = self._get_value(data, old_path)
+        if value is not None:
+            self._delete_value(data, old_path)
+            self._set_value(data, new_path, value)
+
+    def _move_value(
+        self,
+        data: Dict[str, Any],
+        old_path: List[str],
+        new_path: List[str]
+    ):
+        """Move a value from one path to another."""
+        self._rename_key(data, old_path, new_path)
+
+    def _get_value(
+        self,
+        data: Dict[str, Any],
+        path: List[str]
+    ) -> Optional[Any]:
+        """Get a value at the specified path."""
+        current = data
+        for key in path:
+            if key not in current:
+                return None
+            current = current[key]
+        return current
+
+# Create instance for direct function access
+_json_tools = JSONTools()
+read_json = _json_tools.read_json
+write_json = _json_tools.write_json
+merge_json = _json_tools.merge_json
+validate_json = _json_tools.validate_json
+analyze_json = _json_tools.analyze_json
+transform_json = _json_tools.transform_json
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("JSONTools Demonstration")
+    print("==================================================\n")
+    
+    # Sample data
+    data1 = {
+        'id': 1,
+        'name': 'Alice',
+        'scores': [95, 87, 92],
+        'details': {
+            'age': 25,
+            'city': 'New York'
+        }
+    }
+    
+    data2 = {
+        'id': 2,
+        'name': 'Bob',
+        'scores': [88, 90, 85],
+        'details': {
+            'age': 30,
+            'country': 'USA'
+        }
+    }
+    
+    # 1. Write JSON files
+    print("1. Writing JSON Files")
+    print("------------------------------")
+    success = write_json(data1, 'test1.json')
+    print(f"First file written: {success}")
+    success = write_json(data2, 'test2.json')
+    print(f"Second file written: {success}")
+    print()
+    
+    # 2. Read JSON file
+    print("2. Reading JSON File")
+    print("------------------------------")
+    data = read_json('test1.json')
+    print("Contents of test1.json:")
+    print(json.dumps(data, indent=2))
+    print()
+    
+    # 3. Merge JSON files
+    print("3. Merging JSON Files")
+    print("------------------------------")
+    success = merge_json(['test1.json', 'test2.json'], 'merged.json')
+    print(f"Files merged: {success}")
+    if success:
+        print("Merged contents:")
+        print(json.dumps(read_json('merged.json'), indent=2))
+    print()
+    
+    # 4. Validate JSON
+    print("4. Validating JSON")
+    print("------------------------------")
+    schema = {
+        'type': 'object',
+        'properties': {
+            'id': {'type': 'integer'},
+            'name': {'type': 'string'},
+            'scores': {
+                'type': 'array',
+                'items': {'type': 'number'}
+            },
+            'details': {
+                'type': 'object',
+                'properties': {
+                    'age': {'type': 'integer'},
+                    'city': {'type': 'string'}
+                }
+            }
+        },
+        'required': ['id', 'name']
+    }
+    
+    is_valid, error = validate_json(data1, schema)
+    print(f"Validation result: {is_valid}")
+    if error:
+        print(f"Validation error: {error}")
+    print()
+    
+    # 5. Analyze JSON
+    print("5. Analyzing JSON")
+    print("------------------------------")
+    analysis = analyze_json(data1)
+    print("Analysis results:")
+    print(json.dumps(analysis, indent=2))
+    print()
+    
+    # 6. Transform JSON
+    print("6. Transforming JSON")
+    print("------------------------------")
+    transformations = [
+        {
+            'operation': 'set',
+            'path': 'details.status',
+            'value': 'active'
+        },
+        {
+            'operation': 'rename',
+            'path': 'details.city',
+            'value': 'details.location'
+        }
+    ]
+    
+    transformed = transform_json(data1, transformations)
+    print("Transformed data:")
+    print(json.dumps(transformed, indent=2))
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/newspaper_tools.py

@@ -0,0 +1,354 @@
+"""Newspaper tools for scraping and parsing news articles.
+
+Usage:
+from praisonaiagents.tools import newspaper_tools
+article = newspaper_tools.get_article("https://example.com/article")
+sources = newspaper_tools.get_news_sources("technology")
+articles = newspaper_tools.get_articles_from_source("https://techcrunch.com")
+
+or
+from praisonaiagents.tools import get_article, get_news_sources
+article = get_article("https://example.com/article")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import json
+from urllib.parse import urlparse
+
+# Predefined list of popular news sources
+POPULAR_NEWS_SOURCES = {
+    'technology': [
+        'https://techcrunch.com',
+        'https://www.theverge.com',
+        'https://www.wired.com',
+        'https://www.engadget.com',
+        'https://arstechnica.com'
+    ],
+    'business': [
+        'https://www.bloomberg.com',
+        'https://www.reuters.com',
+        'https://www.wsj.com',
+        'https://www.ft.com',
+        'https://www.cnbc.com'
+    ],
+    'general': [
+        'https://www.nytimes.com',
+        'https://www.theguardian.com',
+        'https://www.washingtonpost.com',
+        'https://www.bbc.com',
+        'https://www.cnn.com'
+    ],
+    'sports': [
+        'https://www.espn.com',
+        'https://sports.yahoo.com',
+        'https://www.cbssports.com',
+        'https://www.skysports.com',
+        'https://www.bleacherreport.com'
+    ],
+    'entertainment': [
+        'https://variety.com',
+        'https://www.hollywoodreporter.com',
+        'https://www.ew.com',
+        'https://www.deadline.com',
+        'https://www.imdb.com/news'
+    ],
+    'science': [
+        'https://www.scientificamerican.com',
+        'https://www.sciencedaily.com',
+        'https://www.newscientist.com',
+        'https://www.sciencemag.org',
+        'https://www.nature.com/news'
+    ]
+}
+
+class NewspaperTools:
+    """Tools for scraping and parsing news articles."""
+    
+    def __init__(self):
+        """Initialize NewspaperTools and check for newspaper package."""
+        self._check_newspaper()
+        
+    def _check_newspaper(self):
+        """Check if newspaper package is installed."""
+        if util.find_spec("newspaper") is None:
+            raise ImportError("newspaper3k package is not available. Please install it using: pip install newspaper3k")
+        global newspaper
+        import newspaper
+
+    def get_article(
+        self, 
+        url: str,
+        language: str = 'en'
+    ) -> Dict[str, Any]:
+        """
+        Extract and parse a news article from a URL.
+        
+        Args:
+            url: URL of the article
+            language: Language code (e.g., 'en' for English)
+            
+        Returns:
+            Dict: Article information including title, text, authors, etc.
+        """
+        try:
+            from newspaper import Article, Config
+            
+            # Configure article download
+            config = Config()
+            config.browser_user_agent = 'Mozilla/5.0'
+            config.language = language
+            
+            # Download and parse article
+            article = Article(url, config=config)
+            article.download()
+            article.parse()
+            
+            # Try to extract additional information
+            try:
+                article.nlp()
+            except Exception as e:
+                logging.warning(f"NLP processing failed: {str(e)}")
+            
+            # Build response
+            response = {
+                "url": url,
+                "title": article.title,
+                "text": article.text,
+                "authors": article.authors,
+                "publish_date": article.publish_date.isoformat() if article.publish_date else None,
+                "top_image": article.top_image,
+                "images": list(article.images),
+                "movies": list(article.movies),
+                "source_domain": urlparse(url).netloc,
+            }
+            
+            # Add NLP results if available
+            if hasattr(article, 'keywords') and article.keywords:
+                response["keywords"] = article.keywords
+            if hasattr(article, 'summary') and article.summary:
+                response["summary"] = article.summary
+            
+            return response
+        except Exception as e:
+            error_msg = f"Error extracting article from {url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_news_sources(
+        self, 
+        category: Optional[str] = None,
+        language: str = 'en',
+        country: Optional[str] = None
+    ) -> Union[List[Dict[str, str]], Dict[str, str]]:
+        """
+        Get a list of news sources, optionally filtered by category.
+        
+        Args:
+            category: Category to filter by (e.g., 'technology', 'sports')
+            language: Language code
+            country: Country code
+            
+        Returns:
+            List[Dict] or Dict: List of news sources or error dict
+        """
+        try:
+            sources = []
+            
+            # Get sources for the specified category or all categories
+            if category:
+                category = category.lower()
+                if category in POPULAR_NEWS_SOURCES:
+                    urls = POPULAR_NEWS_SOURCES[category]
+                else:
+                    urls = []
+                    for cat_urls in POPULAR_NEWS_SOURCES.values():
+                        urls.extend(cat_urls)
+            else:
+                urls = []
+                for cat_urls in POPULAR_NEWS_SOURCES.values():
+                    urls.extend(cat_urls)
+            
+            # Create source objects
+            for url in urls:
+                domain = urlparse(url).netloc
+                source = {
+                    "url": url,
+                    "domain": domain,
+                    "name": domain.replace("www.", "").split(".")[0].title(),
+                    "category": category if category else "general"
+                }
+                sources.append(source)
+            
+            return sources
+        except Exception as e:
+            error_msg = f"Error getting news sources: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_articles_from_source(
+        self, 
+        source_url: str,
+        limit: int = 10,
+        language: str = 'en'
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Get recent articles from a news source.
+        
+        Args:
+            source_url: URL of the news source
+            limit: Maximum number of articles to return
+            language: Language code
+            
+        Returns:
+            List[Dict] or Dict: List of articles or error dict
+        """
+        try:
+            from newspaper import Source, Config
+            
+            # Configure source scraping
+            config = Config()
+            config.browser_user_agent = 'Mozilla/5.0'
+            config.language = language
+            config.fetch_images = False  # Speed up processing
+            
+            # Build news source
+            source = Source(source_url, config=config)
+            source.build()
+            
+            # Get articles
+            articles = []
+            for article_url in source.article_urls()[:limit]:
+                try:
+                    article = self.get_article(article_url, language)
+                    if "error" not in article:
+                        articles.append(article)
+                except Exception as e:
+                    logging.warning(f"Error processing article {article_url}: {str(e)}")
+                    continue
+                
+                if len(articles) >= limit:
+                    break
+            
+            return articles
+        except Exception as e:
+            error_msg = f"Error getting articles from {source_url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_trending_topics(
+        self,
+        sources: Optional[List[str]] = None,
+        limit: int = 10,
+        language: str = 'en'
+    ) -> Union[List[str], Dict[str, str]]:
+        """
+        Get trending topics across news sources.
+        
+        Args:
+            sources: List of source URLs to analyze
+            limit: Maximum number of trending topics to return
+            language: Language code
+            
+        Returns:
+            List[str] or Dict: List of trending topics or error dict
+        """
+        try:
+            from collections import Counter
+            
+            # Use default sources if none provided
+            if not sources:
+                sources_data = self.get_news_sources(language=language)
+                if isinstance(sources_data, dict) and "error" in sources_data:
+                    return sources_data
+                sources = [s["url"] for s in sources_data[:5]]  # Use top 5 sources
+            
+            # Collect keywords from articles
+            all_keywords = []
+            for source_url in sources:
+                try:
+                    articles = self.get_articles_from_source(source_url, limit=5, language=language)
+                    if isinstance(articles, list):
+                        for article in articles:
+                            if "keywords" in article:
+                                all_keywords.extend(article["keywords"])
+                except Exception as e:
+                    logging.warning(f"Error processing source {source_url}: {str(e)}")
+                    continue
+            
+            # Get most common keywords
+            trending = Counter(all_keywords).most_common(limit)
+            return [topic for topic, count in trending]
+        except Exception as e:
+            error_msg = f"Error getting trending topics: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+# Create instance for direct function access
+_newspaper_tools = NewspaperTools()
+get_article = _newspaper_tools.get_article
+get_news_sources = _newspaper_tools.get_news_sources
+get_articles_from_source = _newspaper_tools.get_articles_from_source
+get_trending_topics = _newspaper_tools.get_trending_topics
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("NewspaperTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Get news sources
+    print("1. Getting News Sources")
+    print("------------------------------")
+    tech_sources = get_news_sources("technology")
+    print("Technology news sources:")
+    if isinstance(tech_sources, list):
+        print(json.dumps(tech_sources[:3], indent=2))  # Show first 3 sources
+    else:
+        print(tech_sources)  # Show error
+    print()
+    
+    if isinstance(tech_sources, list) and tech_sources:
+        source_url = tech_sources[0]["url"]
+        
+        # 2. Get articles from a source
+        print("2. Getting Articles from Source")
+        print("------------------------------")
+        articles = get_articles_from_source(source_url, limit=2)
+        print(f"Articles from {source_url}:")
+        if isinstance(articles, list):
+            for article in articles:
+                print(f"- {article['title']}")
+                if "summary" in article:
+                    print(f"  Summary: {article['summary'][:200]}...")
+        else:
+            print(articles)  # Show error
+        print()
+        
+        # 3. Get a single article
+        print("3. Getting Single Article")
+        print("------------------------------")
+        if isinstance(articles, list) and articles:
+            article_url = articles[0]["url"]
+            article = get_article(article_url)
+            if "error" not in article:
+                print(f"Article: {article['title']}")
+                if "summary" in article:
+                    print(f"Summary: {article['summary'][:200]}...")
+                print(f"Authors: {', '.join(article['authors'])}")
+                print(f"Date: {article['publish_date']}")
+            else:
+                print(article)  # Show error
+        print()
+        
+        # 4. Get trending topics
+        print("4. Getting Trending Topics")
+        print("------------------------------")
+        topics = get_trending_topics([source_url], limit=5)
+        print("Trending topics:")
+        print(json.dumps(topics, indent=2))
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/pandas_tools.py

@@ -0,0 +1,326 @@
+"""Pandas tools for data manipulation and analysis.
+
+Usage:
+from praisonaiagents.tools import pandas_tools
+df = pandas_tools.read_csv("data.csv")
+df = pandas_tools.filter_data(df, "column > 5")
+summary = pandas_tools.get_summary(df)
+
+or
+from praisonaiagents.tools import read_csv, filter_data, get_summary
+df = read_csv("data.csv")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import json
+import os
+
+# Import pandas for type hints, but don't use it until we check it's installed
+if util.find_spec("pandas") is not None:
+    import pandas as pd
+    import numpy as np
+else:
+    # Create a placeholder for type hints
+    class pd:
+        DataFrame = None
+
+def _convert_to_serializable(obj: Any) -> Any:
+    """Convert numpy/pandas types to JSON serializable Python types."""
+    if isinstance(obj, (np.integer, np.floating)):
+        return obj.item()
+    elif isinstance(obj, np.ndarray):
+        return obj.tolist()
+    elif isinstance(obj, pd.Series):
+        return obj.to_list()
+    elif isinstance(obj, pd.DataFrame):
+        return obj.to_dict(orient='records')
+    return obj
+
+class PandasTools:
+    """Tools for data manipulation and analysis using pandas."""
+    
+    def __init__(self):
+        """Initialize PandasTools and check for pandas installation."""
+        self._check_pandas()
+        
+    def _check_pandas(self):
+        """Check if pandas is installed."""
+        if util.find_spec("pandas") is None:
+            raise ImportError("pandas is not available. Please install it using: pip install pandas")
+        global pd, np
+        import pandas as pd
+        import numpy as np
+
+    def read_csv(self, filepath: str, **kwargs) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Read a CSV file into a pandas DataFrame.
+        
+        Args:
+            filepath: Path to the CSV file
+            **kwargs: Additional arguments to pass to pd.read_csv()
+            
+        Returns:
+            pd.DataFrame or Dict: DataFrame if successful, error dict if failed
+        """
+        try:
+            return pd.read_csv(filepath, **kwargs)
+        except Exception as e:
+            error_msg = f"Error reading CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def read_excel(self, filepath: str, **kwargs) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Read an Excel file into a pandas DataFrame.
+        
+        Args:
+            filepath: Path to the Excel file
+            **kwargs: Additional arguments to pass to pd.read_excel()
+            
+        Returns:
+            pd.DataFrame or Dict: DataFrame if successful, error dict if failed
+        """
+        try:
+            return pd.read_excel(filepath, **kwargs)
+        except Exception as e:
+            error_msg = f"Error reading Excel file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def write_csv(self, df: pd.DataFrame, filepath: str, **kwargs) -> bool:
+        """
+        Write DataFrame to a CSV file.
+        
+        Args:
+            df: DataFrame to write
+            filepath: Output file path
+            **kwargs: Additional arguments to pass to df.to_csv()
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            os.makedirs(os.path.dirname(filepath), exist_ok=True)
+            df.to_csv(filepath, **kwargs)
+            return True
+        except Exception as e:
+            error_msg = f"Error writing CSV file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def write_excel(self, df: pd.DataFrame, filepath: str, **kwargs) -> bool:
+        """
+        Write DataFrame to an Excel file.
+        
+        Args:
+            df: DataFrame to write
+            filepath: Output file path
+            **kwargs: Additional arguments to pass to df.to_excel()
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            os.makedirs(os.path.dirname(filepath), exist_ok=True)
+            df.to_excel(filepath, **kwargs)
+            return True
+        except Exception as e:
+            error_msg = f"Error writing Excel file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def filter_data(self, df: pd.DataFrame, query: str) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Filter DataFrame using a query string.
+        
+        Args:
+            df: Input DataFrame
+            query: Query string (e.g., "column > 5 and other_column == 'value'")
+            
+        Returns:
+            pd.DataFrame or Dict: Filtered DataFrame if successful, error dict if failed
+        """
+        try:
+            return df.query(query)
+        except Exception as e:
+            error_msg = f"Error filtering data with query '{query}': {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_summary(self, df: pd.DataFrame) -> Dict[str, Any]:
+        """
+        Get a summary of the DataFrame including basic statistics and info.
+        
+        Args:
+            df: Input DataFrame
+            
+        Returns:
+            Dict: Summary statistics and information
+        """
+        try:
+            numeric_summary = df.describe().to_dict()
+            # Convert numpy types to native Python types
+            for col in numeric_summary:
+                numeric_summary[col] = {k: _convert_to_serializable(v) 
+                                     for k, v in numeric_summary[col].items()}
+            
+            summary = {
+                "shape": list(df.shape),
+                "columns": list(df.columns),
+                "dtypes": df.dtypes.astype(str).to_dict(),
+                "null_counts": df.isnull().sum().to_dict(),
+                "numeric_summary": numeric_summary,
+                "memory_usage": int(df.memory_usage(deep=True).sum()),
+            }
+            return summary
+        except Exception as e:
+            error_msg = f"Error getting data summary: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def group_by(
+        self, 
+        df: pd.DataFrame, 
+        columns: Union[str, List[str]], 
+        agg_dict: Dict[str, Union[str, List[str]]]
+    ) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Group DataFrame by columns and apply aggregation functions.
+        
+        Args:
+            df: Input DataFrame
+            columns: Column(s) to group by
+            agg_dict: Dictionary of column:function pairs for aggregation
+            
+        Returns:
+            pd.DataFrame or Dict: Grouped DataFrame if successful, error dict if failed
+        """
+        try:
+            return df.groupby(columns).agg(agg_dict).reset_index()
+        except Exception as e:
+            error_msg = f"Error grouping data: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def pivot_table(
+        self, 
+        df: pd.DataFrame, 
+        index: Union[str, List[str]], 
+        columns: Optional[Union[str, List[str]]] = None,
+        values: Optional[Union[str, List[str]]] = None,
+        aggfunc: str = "mean"
+    ) -> Union[pd.DataFrame, Dict[str, str]]:
+        """
+        Create a pivot table from DataFrame.
+        
+        Args:
+            df: Input DataFrame
+            index: Column(s) to use as index
+            columns: Column(s) to use as columns
+            values: Column(s) to aggregate
+            aggfunc: Aggregation function to use
+            
+        Returns:
+            pd.DataFrame or Dict: Pivot table if successful, error dict if failed
+        """
+        try:
+            return pd.pivot_table(
+                df,
+                index=index,
+                columns=columns,
+                values=values,
+                aggfunc=aggfunc
+            ).reset_index()
+        except Exception as e:
+            error_msg = f"Error creating pivot table: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+# Create instance for direct function access
+_pandas_tools = PandasTools()
+read_csv = _pandas_tools.read_csv
+read_excel = _pandas_tools.read_excel
+write_csv = _pandas_tools.write_csv
+write_excel = _pandas_tools.write_excel
+filter_data = _pandas_tools.filter_data
+get_summary = _pandas_tools.get_summary
+group_by = _pandas_tools.group_by
+pivot_table = _pandas_tools.pivot_table
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("PandasTools Demonstration")
+    print("==================================================\n")
+
+    # Create a test directory
+    test_dir = os.path.join(os.getcwd(), "test_files")
+    os.makedirs(test_dir, exist_ok=True)
+    
+    # Create a sample DataFrame
+    df = pd.DataFrame({
+        'name': ['John', 'Jane', 'Bob', 'Alice', 'Charlie'],
+        'age': [25, 30, 35, 28, 32],
+        'city': ['New York', 'London', 'Paris', 'Tokyo', 'London'],
+        'salary': [50000, 60000, 75000, 65000, 55000]
+    })
+    
+    # 1. Write to CSV
+    print("1. Writing to CSV")
+    print("------------------------------")
+    csv_file = os.path.join(test_dir, "sample.csv")
+    success = write_csv(df, csv_file, index=False)
+    print(f"Write successful: {success}\n")
+    
+    # 2. Read from CSV
+    print("2. Reading from CSV")
+    print("------------------------------")
+    df_read = read_csv(csv_file)
+    print("First few rows:")
+    print(df_read.head())
+    print()
+    
+    # 3. Filter Data
+    print("3. Filtering Data")
+    print("------------------------------")
+    filtered_df = filter_data(df, "age > 30 and salary > 60000")
+    print("People over 30 with salary > 60000:")
+    print(filtered_df)
+    print()
+    
+    # 4. Get Summary
+    print("4. Data Summary")
+    print("------------------------------")
+    summary = get_summary(df)
+    print(json.dumps(summary, indent=2))
+    print()
+    
+    # 5. Group By
+    print("5. Group By")
+    print("------------------------------")
+    grouped = group_by(df, "city", {"salary": ["mean", "count"], "age": "mean"})
+    print("Statistics by city:")
+    print(grouped)
+    print()
+    
+    # 6. Pivot Table
+    print("6. Pivot Table")
+    print("------------------------------")
+    pivoted = pivot_table(df, index="city", values=["salary", "age"])
+    print("Pivot table by city:")
+    print(pivoted)
+    print()
+    
+    # Clean up test directory
+    try:
+        import shutil
+        shutil.rmtree(test_dir)
+        print("Test directory cleaned up successfully")
+    except Exception as e:
+        print(f"Error cleaning up test directory: {str(e)}")
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/python_tools.py

@@ -0,0 +1,423 @@
+"""Tools for Python code execution and analysis.
+
+Usage:
+from praisonaiagents.tools import python_tools
+result = python_tools.execute_code("print('Hello, World!')")
+
+or
+from praisonaiagents.tools import execute_code, analyze_code, format_code
+result = execute_code("print('Hello, World!')")
+"""
+
+import logging
+from typing import Dict, List, Optional, Any
+from importlib import util
+import io
+from contextlib import redirect_stdout, redirect_stderr
+import traceback
+
+class PythonTools:
+    """Tools for Python code execution and analysis."""
+    
+    def __init__(self):
+        """Initialize PythonTools."""
+        self._check_dependencies()
+        
+    def _check_dependencies(self):
+        """Check if required packages are installed."""
+        missing = []
+        for package in ['black', 'pylint', 'autopep8']:
+            if util.find_spec(package) is None:
+                missing.append(package)
+        
+        if missing:
+            raise ImportError(
+                f"Required packages not available. Please install: {', '.join(missing)}\n"
+                f"Run: pip install {' '.join(missing)}"
+            )
+
+    def execute_code(
+        self,
+        code: str,
+        globals_dict: Optional[Dict[str, Any]] = None,
+        locals_dict: Optional[Dict[str, Any]] = None,
+        timeout: int = 30,
+        max_output_size: int = 10000
+    ) -> Dict[str, Any]:
+        """Execute Python code safely."""
+        try:
+            # Set up execution environment
+            if globals_dict is None:
+                globals_dict = {'__builtins__': __builtins__}
+            if locals_dict is None:
+                locals_dict = {}
+            
+            # Capture output
+            stdout_buffer = io.StringIO()
+            stderr_buffer = io.StringIO()
+            
+            try:
+                # Compile code
+                compiled_code = compile(code, '<string>', 'exec')
+                
+                # Execute with output capture
+                with redirect_stdout(stdout_buffer), redirect_stderr(stderr_buffer):
+                    exec(compiled_code, globals_dict, locals_dict)
+                    
+                    # Get last expression value if any
+                    import ast
+                    tree = ast.parse(code)
+                    if tree.body and isinstance(tree.body[-1], ast.Expr):
+                        result = eval(
+                            compile(ast.Expression(tree.body[-1].value), '<string>', 'eval'),
+                            globals_dict,
+                            locals_dict
+                        )
+                    else:
+                        result = None
+                
+                # Get output
+                stdout = stdout_buffer.getvalue()
+                stderr = stderr_buffer.getvalue()
+                
+                # Truncate output if too large
+                if len(stdout) > max_output_size:
+                    stdout = stdout[:max_output_size] + "...[truncated]"
+                if len(stderr) > max_output_size:
+                    stderr = stderr[:max_output_size] + "...[truncated]"
+                
+                return {
+                    'result': result,
+                    'stdout': stdout,
+                    'stderr': stderr,
+                    'success': True
+                }
+            
+            except Exception as e:
+                error_msg = f"Error executing code: {str(e)}"
+                logging.error(error_msg)
+                return {
+                    'result': None,
+                    'stdout': stdout_buffer.getvalue(),
+                    'stderr': error_msg,
+                    'success': False
+                }
+            
+        except Exception as e:
+            error_msg = f"Error executing code: {str(e)}"
+            logging.error(error_msg)
+            return {
+                'result': None,
+                'stdout': '',
+                'stderr': error_msg,
+                'success': False
+            }
+
+    def analyze_code(
+        self,
+        code: str
+    ) -> Optional[Dict[str, Any]]:
+        """Analyze Python code structure and quality.
+        
+        Args:
+            code: Python code to analyze
+            
+        Returns:
+            Dictionary with analysis results
+        """
+        try:
+            # Import ast only when needed
+            import ast
+            
+            # Parse code
+            tree = ast.parse(code)
+            
+            # Analyze structure
+            analysis = {
+                'imports': [],
+                'functions': [],
+                'classes': [],
+                'variables': [],
+                'complexity': {
+                    'lines': len(code.splitlines()),
+                    'functions': 0,
+                    'classes': 0,
+                    'branches': 0
+                }
+            }
+            
+            # Analyze nodes
+            for node in ast.walk(tree):
+                if isinstance(node, ast.Import):
+                    for name in node.names:
+                        analysis['imports'].append(name.name)
+                elif isinstance(node, ast.ImportFrom):
+                    module = node.module or ''
+                    for name in node.names:
+                        analysis['imports'].append(f"{module}.{name.name}")
+                elif isinstance(node, ast.FunctionDef):
+                    analysis['functions'].append({
+                        'name': node.name,
+                        'args': [
+                            arg.arg for arg in node.args.args
+                        ],
+                        'decorators': [
+                            ast.unparse(d) for d in node.decorator_list
+                        ]
+                    })
+                    analysis['complexity']['functions'] += 1
+                elif isinstance(node, ast.ClassDef):
+                    analysis['classes'].append({
+                        'name': node.name,
+                        'bases': [
+                            ast.unparse(base) for base in node.bases
+                        ],
+                        'decorators': [
+                            ast.unparse(d) for d in node.decorator_list
+                        ]
+                    })
+                    analysis['complexity']['classes'] += 1
+                elif isinstance(node, ast.Assign):
+                    for target in node.targets:
+                        if isinstance(target, ast.Name):
+                            analysis['variables'].append(target.id)
+                elif isinstance(node, (ast.If, ast.While, ast.For)):
+                    analysis['complexity']['branches'] += 1
+            
+            return analysis
+        except Exception as e:
+            error_msg = f"Error analyzing code: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def format_code(
+        self,
+        code: str,
+        style: str = 'black',
+        line_length: int = 88
+    ) -> Optional[str]:
+        """Format Python code according to style guide.
+        
+        Args:
+            code: Python code to format
+            style: Formatting style ('black' or 'pep8')
+            line_length: Maximum line length
+            
+        Returns:
+            Formatted code
+        """
+        try:
+            if util.find_spec(style) is None:
+                error_msg = f"{style} package is not available. Please install it using: pip install {style}"
+                logging.error(error_msg)
+                return None
+
+            if style == 'black':
+                import black
+                return black.format_str(
+                    code,
+                    mode=black.FileMode(
+                        line_length=line_length
+                    )
+                )
+            else:  # pep8
+                import autopep8
+                return autopep8.fix_code(
+                    code,
+                    options={
+                        'max_line_length': line_length
+                    }
+                )
+        except Exception as e:
+            error_msg = f"Error formatting code: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def lint_code(
+        self,
+        code: str
+    ) -> Optional[Dict[str, List[Dict[str, Any]]]]:
+        """Lint Python code for potential issues.
+        
+        Args:
+            code: Python code to lint
+            
+        Returns:
+            Dictionary with linting results
+        """
+        try:
+            if util.find_spec('pylint') is None:
+                error_msg = "pylint package is not available. Please install it using: pip install pylint"
+                logging.error(error_msg)
+                return None
+
+            # Import pylint only when needed
+            from pylint.reporters import JSONReporter
+            from pylint.lint.run import Run
+            
+            # Create temporary file for pylint
+            import tempfile
+            with tempfile.NamedTemporaryFile(
+                mode='w',
+                suffix='.py',
+                delete=False
+            ) as f:
+                f.write(code)
+                temp_path = f.name
+            
+            # Run pylint
+            reporter = JSONReporter()
+            Run(
+                [temp_path],
+                reporter=reporter,
+                exit=False
+            )
+            
+            # Process results
+            results = {
+                'errors': [],
+                'warnings': [],
+                'conventions': []
+            }
+            
+            for msg in reporter.messages:
+                item = {
+                    'type': msg.category,
+                    'module': msg.module,
+                    'obj': msg.obj,
+                    'line': msg.line,
+                    'column': msg.column,
+                    'path': msg.path,
+                    'symbol': msg.symbol,
+                    'message': msg.msg,
+                    'message-id': msg.msg_id
+                }
+                
+                if msg.category in ['error', 'fatal']:
+                    results['errors'].append(item)
+                elif msg.category == 'warning':
+                    results['warnings'].append(item)
+                else:
+                    results['conventions'].append(item)
+            
+            # Clean up
+            import os
+            os.unlink(temp_path)
+            
+            return results
+        except Exception as e:
+            error_msg = f"Error linting code: {str(e)}"
+            logging.error(error_msg)
+            return {
+                'errors': [],
+                'warnings': [],
+                'conventions': []
+            }
+
+    def disassemble_code(
+        self,
+        code: str
+    ) -> Optional[str]:
+        """Disassemble Python code to bytecode.
+        
+        Args:
+            code: Python code to disassemble
+            
+        Returns:
+            Disassembled bytecode as string
+        """
+        try:
+            # Import dis only when needed
+            import dis
+            
+            # Compile code
+            compiled_code = compile(code, '<string>', 'exec')
+            
+            # Capture disassembly
+            output = io.StringIO()
+            with redirect_stdout(output):
+                dis.dis(compiled_code)
+            
+            return output.getvalue()
+        except Exception as e:
+            error_msg = f"Error disassembling code: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+# Create instance for direct function access
+_python_tools = PythonTools()
+execute_code = _python_tools.execute_code
+analyze_code = _python_tools.analyze_code
+format_code = _python_tools.format_code
+lint_code = _python_tools.lint_code
+disassemble_code = _python_tools.disassemble_code
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("PythonTools Demonstration")
+    print("==================================================\n")
+
+    print("1. Execute Python Code")
+    print("------------------------------")
+    code = """
+def greet(name):
+    return f"Hello, {name}!"
+
+result = greet("World")
+print(result)
+"""
+    print("Code to execute:")
+    print(code)
+    print("\nOutput:")
+    result = execute_code(code)
+    print(result["stdout"])
+    print()
+
+    print("2. Format Python Code")
+    print("------------------------------")
+    code = """
+def messy_function(x,y,   z):
+    if x>0:
+     return y+z
+    else:
+     return y-z
+"""
+    print("Before formatting:")
+    print(code)
+    print("\nAfter formatting:")
+    result = format_code(code)
+    print(result)
+    print()
+
+    print("3. Lint Python Code")
+    print("------------------------------")
+    code = """
+def bad_function():
+    unused_var = 42
+    return 'result'
+"""
+    print("Code to lint:")
+    print(code)
+    print("\nLinting results:")
+    result = lint_code(code)
+    print(result)
+    print()
+
+    print("4. Execute Code with Variables")
+    print("------------------------------")
+    code = """
+x = 10
+y = 20
+result = x + y
+print(f"The sum of {x} and {y} is {result}")
+"""
+    print("Code to execute:")
+    print(code)
+    print("\nOutput:")
+    result = execute_code(code)
+    print(result["stdout"])
+    print()
+
+    print("==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/shell_tools.py

@@ -0,0 +1,278 @@
+"""Tools for executing shell commands safely.
+
+This module provides a safe interface for executing shell commands with:
+- Timeout control
+- Output capture
+- Error handling
+- Resource limits
+"""
+
+import subprocess
+import shlex
+import logging
+import os
+import time
+from typing import Dict, List, Optional, Union
+
+class ShellTools:
+    """Tools for executing shell commands safely."""
+    
+    def __init__(self):
+        """Initialize ShellTools."""
+        self._check_dependencies()
+    
+    def _check_dependencies(self):
+        """Check if required packages are installed."""
+        try:
+            import psutil
+        except ImportError:
+            raise ImportError(
+                "Required package not available. Please install: psutil\n"
+                "Run: pip install psutil"
+            )
+    
+    def execute_command(
+        self,
+        command: str,
+        cwd: Optional[str] = None,
+        timeout: int = 30,
+        shell: bool = False,
+        env: Optional[Dict[str, str]] = None,
+        max_output_size: int = 10000
+    ) -> Dict[str, Union[str, int, bool]]:
+        """Execute a shell command safely.
+        
+        Args:
+            command: Command to execute
+            cwd: Working directory
+            timeout: Maximum execution time in seconds
+            shell: Whether to run command in shell
+            env: Environment variables
+            max_output_size: Maximum output size in bytes
+            
+        Returns:
+            Dictionary with execution results
+        """
+        try:
+            # Split command if not using shell
+            if not shell:
+                command = shlex.split(command)
+            
+            # Set up process environment
+            process_env = os.environ.copy()
+            if env:
+                process_env.update(env)
+            
+            # Start process
+            start_time = time.time()
+            process = subprocess.Popen(
+                command,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+                cwd=cwd,
+                shell=shell,
+                env=process_env,
+                text=True
+            )
+            
+            try:
+                # Wait for process with timeout
+                stdout, stderr = process.communicate(timeout=timeout)
+                
+                # Truncate output if too large
+                if len(stdout) > max_output_size:
+                    stdout = stdout[:max_output_size] + "...[truncated]"
+                if len(stderr) > max_output_size:
+                    stderr = stderr[:max_output_size] + "...[truncated]"
+                
+                return {
+                    'stdout': stdout,
+                    'stderr': stderr,
+                    'exit_code': process.returncode,
+                    'success': process.returncode == 0,
+                    'execution_time': time.time() - start_time
+                }
+            
+            except subprocess.TimeoutExpired:
+                # Kill process on timeout
+                parent = psutil.Process(process.pid)
+                children = parent.children(recursive=True)
+                for child in children:
+                    child.kill()
+                parent.kill()
+                
+                return {
+                    'stdout': '',
+                    'stderr': f'Command timed out after {timeout} seconds',
+                    'exit_code': -1,
+                    'success': False,
+                    'execution_time': timeout
+                }
+                
+        except Exception as e:
+            error_msg = f"Error executing command: {str(e)}"
+            logging.error(error_msg)
+            return {
+                'stdout': '',
+                'stderr': error_msg,
+                'exit_code': -1,
+                'success': False,
+                'execution_time': 0
+            }
+    
+    def list_processes(self) -> List[Dict[str, Union[int, str, float]]]:
+        """List running processes with their details.
+        
+        Returns:
+            List of process information dictionaries
+        """
+        try:
+            processes = []
+            for proc in psutil.process_iter(['pid', 'name', 'username', 'memory_percent', 'cpu_percent']):
+                try:
+                    pinfo = proc.info
+                    processes.append({
+                        'pid': pinfo['pid'],
+                        'name': pinfo['name'],
+                        'username': pinfo['username'],
+                        'memory_percent': round(pinfo['memory_percent'], 2),
+                        'cpu_percent': round(pinfo['cpu_percent'], 2)
+                    })
+                except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess):
+                    pass
+            return processes
+        except Exception as e:
+            error_msg = f"Error listing processes: {str(e)}"
+            logging.error(error_msg)
+            return []
+    
+    def kill_process(
+        self,
+        pid: int,
+        force: bool = False
+    ) -> Dict[str, Union[bool, str]]:
+        """Kill a process by its PID.
+        
+        Args:
+            pid: Process ID to kill
+            force: Whether to force kill (-9)
+            
+        Returns:
+            Dictionary with operation results
+        """
+        try:
+            process = psutil.Process(pid)
+            if force:
+                process.kill()  # SIGKILL
+            else:
+                process.terminate()  # SIGTERM
+            
+            return {
+                'success': True,
+                'message': f'Process {pid} killed successfully'
+            }
+        except psutil.NoSuchProcess:
+            return {
+                'success': False,
+                'message': f'No process found with PID {pid}'
+            }
+        except psutil.AccessDenied:
+            return {
+                'success': False,
+                'message': f'Access denied to kill process {pid}'
+            }
+        except Exception as e:
+            error_msg = f"Error killing process: {str(e)}"
+            logging.error(error_msg)
+            return {
+                'success': False,
+                'message': error_msg
+            }
+    
+    def get_system_info(self) -> Dict[str, Union[float, int, str, Dict]]:
+        """Get system information.
+        
+        Returns:
+            Dictionary with system information
+        """
+        try:
+            cpu_percent = psutil.cpu_percent(interval=1)
+            memory = psutil.virtual_memory()
+            disk = psutil.disk_usage('/')
+            
+            return {
+                'cpu': {
+                    'percent': cpu_percent,
+                    'cores': psutil.cpu_count(),
+                    'physical_cores': psutil.cpu_count(logical=False)
+                },
+                'memory': {
+                    'total': memory.total,
+                    'available': memory.available,
+                    'percent': memory.percent,
+                    'used': memory.used,
+                    'free': memory.free
+                },
+                'disk': {
+                    'total': disk.total,
+                    'used': disk.used,
+                    'free': disk.free,
+                    'percent': disk.percent
+                },
+                'boot_time': psutil.boot_time(),
+                'platform': os.uname().sysname
+            }
+        except Exception as e:
+            error_msg = f"Error getting system info: {str(e)}"
+            logging.error(error_msg)
+            return {}
+
+# Create instance for direct function access
+_shell_tools = ShellTools()
+execute_command = _shell_tools.execute_command
+list_processes = _shell_tools.list_processes
+kill_process = _shell_tools.kill_process
+get_system_info = _shell_tools.get_system_info
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("ShellTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Execute command
+    print("1. Command Execution")
+    print("------------------------------")
+    result = execute_command("ls -la")
+    print(f"Success: {result['success']}")
+    print(f"Output:\n{result['stdout']}")
+    if result['stderr']:
+        print(f"Errors:\n{result['stderr']}")
+    print(f"Execution time: {result['execution_time']:.2f}s")
+    print()
+    
+    # 2. System Information
+    print("2. System Information")
+    print("------------------------------")
+    info = get_system_info()
+    print(f"CPU Usage: {info['cpu']['percent']}%")
+    print(f"Memory Usage: {info['memory']['percent']}%")
+    print(f"Disk Usage: {info['disk']['percent']}%")
+    print(f"Platform: {info['platform']}")
+    print()
+    
+    # 3. Process List
+    print("3. Process List (top 5 by CPU)")
+    print("------------------------------")
+    processes = sorted(
+        list_processes(),
+        key=lambda x: x['cpu_percent'],
+        reverse=True
+    )[:5]
+    for proc in processes:
+        print(f"PID: {proc['pid']}, Name: {proc['name']}, CPU: {proc['cpu_percent']}%")
+    print()
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/spider_tools.py

@@ -0,0 +1,431 @@
+"""Tools for web scraping and crawling.
+
+Usage:
+from praisonaiagents.tools import spider_tools
+content = spider_tools.scrape_page("https://example.com")
+links = spider_tools.extract_links("https://example.com")
+
+or
+from praisonaiagents.tools import scrape_page, extract_links
+content = scrape_page("https://example.com")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import json
+from urllib.parse import urljoin, urlparse
+import re
+import os
+import hashlib
+import time
+
+class SpiderTools:
+    """Tools for web scraping and crawling."""
+    
+    def __init__(self):
+        """Initialize SpiderTools and check for required packages."""
+        self._session = None
+        
+    def _get_session(self):
+        """Get or create requests session with common headers."""
+        if util.find_spec('requests') is None:
+            error_msg = "requests package is not available. Please install it using: pip install requests"
+            logging.error(error_msg)
+            return None
+            
+        if self._session is None:
+            import requests
+            self._session = requests.Session()
+            self._session.headers.update({
+                'User-Agent': 'Mozilla/5.0 (compatible; PraisonAI/1.0; +http://praisonai.com/bot)',
+                'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
+                'Accept-Language': 'en-US,en;q=0.5',
+                'Accept-Encoding': 'gzip, deflate, br',
+                'Connection': 'keep-alive',
+            })
+        return self._session
+
+    def scrape_page(
+        self,
+        url: str,
+        selector: Optional[str] = None,
+        extract_images: bool = False,
+        extract_links: bool = False,
+        timeout: int = 30,
+        verify_ssl: bool = True
+    ) -> Union[Dict[str, Any], Dict[str, str]]:
+        """
+        Scrape content from a webpage.
+        
+        Args:
+            url: URL to scrape
+            selector: Optional CSS selector to extract specific content
+            extract_images: Whether to extract image URLs
+            extract_links: Whether to extract links
+            timeout: Request timeout in seconds
+            verify_ssl: Whether to verify SSL certificates
+            
+        Returns:
+            Dict: Scraped content or error dict
+        """
+        try:
+            session = self._get_session()
+            if session is None:
+                return {"error": "requests package not available"}
+
+            # Import BeautifulSoup only when needed
+            if util.find_spec('bs4') is None:
+                error_msg = "bs4 package is not available. Please install it using: pip install beautifulsoup4"
+                logging.error(error_msg)
+                return {"error": error_msg}
+            from bs4 import BeautifulSoup
+
+            # Make request
+            response = session.get(
+                url,
+                timeout=timeout,
+                verify=verify_ssl
+            )
+            response.raise_for_status()
+            
+            # Parse HTML
+            soup = BeautifulSoup(response.text, 'lxml')
+            
+            # Remove unwanted elements
+            for element in soup(['script', 'style']):
+                element.decompose()
+            
+            # Initialize result
+            result = {
+                'url': url,
+                'status_code': response.status_code,
+                'encoding': response.encoding,
+                'headers': dict(response.headers),
+            }
+            
+            # Extract content based on selector
+            if selector:
+                elements = soup.select(selector)
+                result['content'] = [elem.get_text(strip=True) for elem in elements]
+                result['html'] = [str(elem) for elem in elements]
+            else:
+                result['title'] = soup.title.string if soup.title else None
+                result['content'] = soup.get_text(separator=' ', strip=True)
+                result['html'] = str(soup)
+            
+            # Extract metadata
+            meta_tags = {}
+            for meta in soup.find_all('meta'):
+                name = meta.get('name') or meta.get('property')
+                if name:
+                    meta_tags[name] = meta.get('content')
+            result['meta_tags'] = meta_tags
+            
+            # Extract images if requested
+            if extract_images:
+                images = []
+                for img in soup.find_all('img'):
+                    src = img.get('src')
+                    if src:
+                        images.append({
+                            'src': urljoin(url, src),
+                            'alt': img.get('alt', ''),
+                            'title': img.get('title', '')
+                        })
+                result['images'] = images
+            
+            # Extract links if requested
+            if extract_links:
+                links = []
+                for link in soup.find_all('a'):
+                    href = link.get('href')
+                    if href:
+                        links.append({
+                            'url': urljoin(url, href),
+                            'text': link.get_text(strip=True),
+                            'title': link.get('title', '')
+                        })
+                result['links'] = links
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error scraping {url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def extract_links(
+        self,
+        url: str,
+        same_domain: bool = True,
+        exclude_patterns: Optional[List[str]] = None,
+        timeout: int = 30,
+        verify_ssl: bool = True
+    ) -> Union[List[Dict[str, str]], Dict[str, str]]:
+        """
+        Extract all links from a webpage.
+        
+        Args:
+            url: URL to extract links from
+            same_domain: Only return links from the same domain
+            exclude_patterns: List of regex patterns to exclude
+            timeout: Request timeout in seconds
+            verify_ssl: Whether to verify SSL certificates
+            
+        Returns:
+            List[Dict] or Dict: List of links or error dict
+        """
+        try:
+            # Compile exclude patterns
+            if exclude_patterns:
+                exclude_patterns = [re.compile(p) for p in exclude_patterns]
+            
+            # Get base domain
+            base_domain = urlparse(url).netloc
+            
+            # Scrape page
+            result = self.scrape_page(
+                url,
+                extract_links=True,
+                timeout=timeout,
+                verify_ssl=verify_ssl
+            )
+            
+            if "error" in result:
+                return result
+            
+            # Filter and clean links
+            links = []
+            seen_urls = set()
+            
+            for link in result.get('links', []):
+                link_url = link['url']
+                
+                # Skip if already seen
+                if link_url in seen_urls:
+                    continue
+                
+                # Parse URL
+                parsed = urlparse(link_url)
+                
+                # Skip if not same domain and same_domain is True
+                if same_domain and parsed.netloc != base_domain:
+                    continue
+                
+                # Skip if matches exclude patterns
+                if exclude_patterns and any(p.search(link_url) for p in exclude_patterns):
+                    continue
+                
+                # Add to results
+                links.append(link)
+                seen_urls.add(link_url)
+            
+            return links
+        except Exception as e:
+            error_msg = f"Error extracting links from {url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def crawl(
+        self,
+        start_url: str,
+        max_pages: int = 10,
+        same_domain: bool = True,
+        exclude_patterns: Optional[List[str]] = None,
+        delay: float = 1.0,
+        timeout: int = 30,
+        verify_ssl: bool = True,
+        output_dir: Optional[str] = None
+    ) -> Union[List[Dict[str, Any]], Dict[str, str]]:
+        """
+        Crawl multiple pages starting from a URL.
+        
+        Args:
+            start_url: Starting URL
+            max_pages: Maximum number of pages to crawl
+            same_domain: Only crawl pages from the same domain
+            exclude_patterns: List of regex patterns to exclude
+            delay: Delay between requests in seconds
+            timeout: Request timeout in seconds
+            verify_ssl: Whether to verify SSL certificates
+            output_dir: Directory to save crawled pages
+            
+        Returns:
+            List[Dict] or Dict: Crawled pages or error dict
+        """
+        try:
+            # Create output directory if needed
+            if output_dir:
+                os.makedirs(output_dir, exist_ok=True)
+            
+            # Initialize crawl state
+            to_visit = {start_url}
+            visited = set()
+            results = []
+            
+            while to_visit and len(visited) < max_pages:
+                # Get next URL
+                url = to_visit.pop()
+                
+                # Skip if already visited
+                if url in visited:
+                    continue
+                
+                # Add to visited
+                visited.add(url)
+                
+                # Delay if not first request
+                if len(visited) > 1:
+                    time.sleep(delay)
+                
+                # Scrape page
+                result = self.scrape_page(
+                    url,
+                    extract_links=True,
+                    timeout=timeout,
+                    verify_ssl=verify_ssl
+                )
+                
+                if "error" in result:
+                    logging.warning(f"Error crawling {url}: {result['error']}")
+                    continue
+                
+                # Save result
+                results.append(result)
+                
+                # Save to file if requested
+                if output_dir:
+                    filename = hashlib.md5(url.encode()).hexdigest() + '.json'
+                    filepath = os.path.join(output_dir, filename)
+                    with open(filepath, 'w', encoding='utf-8') as f:
+                        json.dump(result, f, indent=2, ensure_ascii=False)
+                
+                # Add new links to visit
+                for link in result.get('links', []):
+                    link_url = link['url']
+                    parsed = urlparse(link_url)
+                    
+                    # Skip if not same domain and same_domain is True
+                    if same_domain and parsed.netloc != urlparse(start_url).netloc:
+                        continue
+                    
+                    # Skip if matches exclude patterns
+                    if exclude_patterns and any(
+                        re.compile(p).search(link_url) for p in exclude_patterns
+                    ):
+                        continue
+                    
+                    # Add to visit if not visited
+                    if link_url not in visited:
+                        to_visit.add(link_url)
+            
+            return results
+        except Exception as e:
+            error_msg = f"Error crawling from {start_url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def extract_text(
+        self,
+        url: str,
+        selector: Optional[str] = None,
+        timeout: int = 30,
+        verify_ssl: bool = True
+    ) -> Union[str, Dict[str, str]]:
+        """
+        Extract clean text content from a webpage.
+        
+        Args:
+            url: URL to extract text from
+            selector: Optional CSS selector to extract specific content
+            timeout: Request timeout in seconds
+            verify_ssl: Whether to verify SSL certificates
+            
+        Returns:
+            str or Dict: Extracted text or error dict
+        """
+        try:
+            result = self.scrape_page(
+                url,
+                selector=selector,
+                timeout=timeout,
+                verify_ssl=verify_ssl
+            )
+            
+            if "error" in result:
+                return result
+            
+            if selector:
+                return '\n'.join(result['content'])
+            return result['content']
+        except Exception as e:
+            error_msg = f"Error extracting text from {url}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+# Create instance for direct function access
+_spider_tools = SpiderTools()
+scrape_page = _spider_tools.scrape_page
+extract_links = _spider_tools.extract_links
+crawl = _spider_tools.crawl
+extract_text = _spider_tools.extract_text
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("SpiderTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Scrape a webpage
+    print("1. Scraping Webpage")
+    print("------------------------------")
+    url = "https://example.com"
+    result = scrape_page(url, extract_images=True, extract_links=True)
+    print(f"Content from {url}:")
+    if "error" not in result:
+        print(f"Title: {result['title']}")
+        print(f"Content length: {len(result['content'])} characters")
+        print(f"Number of images: {len(result.get('images', []))}")
+        print(f"Number of links: {len(result.get('links', []))}")
+    else:
+        print(result)  # Show error
+    print()
+    
+    # 2. Extract links
+    print("2. Extracting Links")
+    print("------------------------------")
+    links = extract_links(url)
+    print(f"Links from {url}:")
+    if isinstance(links, list):
+        for link in links:
+            print(f"- {link['url']} ({link['text']})")
+    else:
+        print(links)  # Show error
+    print()
+    
+    # 3. Extract text
+    print("3. Extracting Text")
+    print("------------------------------")
+    text = extract_text(url)
+    print(f"Text from {url}:")
+    if isinstance(text, str):
+        print(text[:500] + "..." if len(text) > 500 else text)
+    else:
+        print(text)  # Show error
+    print()
+    
+    # 4. Crawl multiple pages
+    print("4. Crawling Multiple Pages")
+    print("------------------------------")
+    results = crawl(url, max_pages=2, delay=1.0)
+    print(f"Crawl results from {url}:")
+    if isinstance(results, list):
+        print(f"Crawled {len(results)} pages")
+        for result in results:
+            print(f"- {result['url']}: {result['title']}")
+    else:
+        print(results)  # Show error
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/test.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+"""Test runner for all tools."""
+
+import os
+import glob
+import logging
+import subprocess
+
+# Set up logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+def main():
+    """Run all tool files."""
+    logger.info("Starting tests...")
+    
+    # Get all *_tools.py files
+    tools_dir = os.path.dirname(os.path.abspath(__file__))
+    tool_files = glob.glob(os.path.join(tools_dir, "*_tools.py"))
+    
+    # Run each tool file
+    for tool_file in sorted(tool_files):
+        module_name = os.path.basename(tool_file)
+        logger.info(f"\nRunning {module_name}...")
+        
+        try:
+            # Run the tool file directly
+            result = subprocess.run(
+                ["python3", tool_file],
+                capture_output=True,
+                text=True,
+                cwd=tools_dir
+            )
+            
+            # Log output
+            if result.stdout:
+                logger.info(f"Output:\n{result.stdout}")
+            if result.stderr:
+                logger.error(f"Errors:\n{result.stderr}")
+            
+            if result.returncode == 0:
+                logger.info(f" {module_name} completed successfully")
+            else:
+                logger.error(f" {module_name} failed with return code {result.returncode}")
+        
+        except Exception as e:
+            logger.error(f"Error running {module_name}: {str(e)}")
+            continue
+    
+    logger.info("\nAll tests completed!")
+
+if __name__ == "__main__":
+    main()

agents/praisonaiagents/tools/tools.py

@@ -0,0 +1,9 @@
+"""Tools module for PraisonAI Agents"""
+from .duckduckgo_tools import internet_search
+
+class Tools:
+    """Tools class for backward compatibility"""
+    internet_search = staticmethod(internet_search)
+
+# Re-export the function
+__all__ = ['Tools', 'internet_search']

agents/praisonaiagents/tools/wikipedia_tools.py

@@ -0,0 +1,272 @@
+"""Wikipedia tools for accessing and searching Wikipedia content.
+
+Usage:
+from praisonaiagents.tools import wikipedia_tools
+summary = wikipedia_tools.wiki_summary("Python programming language")
+page = wikipedia_tools.wiki_page("Python programming language")
+results = wikipedia_tools.wiki_search("Python programming")
+
+or
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page
+summary = wiki_summary("Python programming language")
+"""
+
+import logging
+from typing import List, Dict, Union, Any
+from importlib import util
+import json
+
+class WikipediaTools:
+    """Tools for accessing and searching Wikipedia content."""
+    
+    def __init__(self):
+        """Initialize WikipediaTools and check for wikipedia package."""
+        self._check_wikipedia()
+        
+    def _check_wikipedia(self):
+        """Check if wikipedia package is installed."""
+        if util.find_spec("wikipedia") is None:
+            raise ImportError("wikipedia package is not available. Please install it using: pip install wikipedia")
+        global wikipedia
+        import wikipedia
+        # Set default language to English
+        wikipedia.set_lang("en")
+
+    def wiki_search(
+        self, 
+        query: str, 
+        results: int = 10, 
+        suggestion: bool = True
+    ) -> Union[List[str], Dict[str, str]]:
+        """
+        Search Wikipedia for a query.
+        
+        Args:
+            query: Search query
+            results: Maximum number of results to return
+            suggestion: Whether to suggest similar queries
+            
+        Returns:
+            List[str] or Dict: List of search results or error dict
+        """
+        try:
+            search_results = wikipedia.search(query, results=results, suggestion=suggestion)
+            if isinstance(search_results, tuple):
+                # If suggestion is True, returns (results, suggestion)
+                return {
+                    "results": search_results[0],
+                    "suggestion": search_results[1] if search_results[1] else None
+                }
+            return {"results": search_results, "suggestion": None}
+        except Exception as e:
+            error_msg = f"Error searching Wikipedia for '{query}': {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def wiki_summary(
+        self, 
+        title: str, 
+        sentences: int = 5, 
+        auto_suggest: bool = True
+    ) -> Union[str, Dict[str, str]]:
+        """
+        Get a summary of a Wikipedia page.
+        
+        Args:
+            title: Title of the Wikipedia page
+            sentences: Number of sentences to return
+            auto_suggest: Whether to auto-suggest similar titles
+            
+        Returns:
+            str or Dict: Page summary if successful, error dict if failed
+        """
+        try:
+            return wikipedia.summary(title, sentences=sentences, auto_suggest=auto_suggest)
+        except wikipedia.exceptions.DisambiguationError as e:
+            return {
+                "error": "Disambiguation page",
+                "options": e.options[:10]  # Limit to first 10 options
+            }
+        except Exception as e:
+            error_msg = f"Error getting summary for '{title}': {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def wiki_page(
+        self, 
+        title: str, 
+        auto_suggest: bool = True
+    ) -> Union[Dict[str, Any], Dict[str, str]]:
+        """
+        Get detailed information about a Wikipedia page.
+        
+        Args:
+            title: Title of the Wikipedia page
+            auto_suggest: Whether to auto-suggest similar titles
+            
+        Returns:
+            Dict: Page information if successful, error dict if failed
+        """
+        try:
+            page = wikipedia.page(title, auto_suggest=auto_suggest)
+            return {
+                "title": page.title,
+                "url": page.url,
+                "content": page.content,
+                "summary": page.summary,
+                "references": page.references,
+                "categories": page.categories,
+                "links": page.links,
+                "images": page.images,
+                "sections": self.get_sections(page)
+            }
+        except wikipedia.exceptions.DisambiguationError as e:
+            return {
+                "error": "Disambiguation page",
+                "options": e.options[:10]  # Limit to first 10 options
+            }
+        except Exception as e:
+            error_msg = f"Error getting page for '{title}': {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_sections(self, page: Any) -> List[Dict[str, Any]]:
+        """
+        Extract sections and their content from a Wikipedia page.
+        
+        Args:
+            page: Wikipedia page object
+            
+        Returns:
+            List[Dict]: List of sections with their titles and content
+        """
+        sections = []
+        content = page.content
+        section_titles = [s.strip() for s in content.split("==")[1::2]]
+        section_contents = [s.strip() for s in content.split("==")[2::2]]
+        
+        # Add introduction section
+        intro = content.split("==")[0].strip()
+        if intro:
+            sections.append({
+                "title": "Introduction",
+                "level": 0,
+                "content": intro
+            })
+        
+        # Process remaining sections
+        for title, content in zip(section_titles, section_contents):
+            # Count leading '=' to determine section level
+            level = title.count("=") // 2
+            clean_title = title.strip("= ")
+            
+            sections.append({
+                "title": clean_title,
+                "level": level,
+                "content": content.strip()
+            })
+        
+        return sections
+
+    def wiki_random(self, pages: int = 1) -> Union[List[str], Dict[str, str]]:
+        """
+        Get random Wikipedia page titles.
+        
+        Args:
+            pages: Number of random pages to return
+            
+        Returns:
+            List[str] or Dict: List of random page titles or error dict
+        """
+        try:
+            return wikipedia.random(pages)
+        except Exception as e:
+            error_msg = f"Error getting random pages: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def wiki_language(self, language: str) -> bool:
+        """
+        Set the language for Wikipedia searches.
+        
+        Args:
+            language: Language code (e.g., 'en' for English, 'es' for Spanish)
+            
+        Returns:
+            bool: True if successful, False if failed
+        """
+        try:
+            wikipedia.set_lang(language)
+            return True
+        except Exception as e:
+            error_msg = f"Error setting language to '{language}': {str(e)}"
+            logging.error(error_msg)
+            return False
+
+# Create instance for direct function access
+_wikipedia_tools = WikipediaTools()
+wiki_search = _wikipedia_tools.wiki_search
+wiki_summary = _wikipedia_tools.wiki_summary
+wiki_page = _wikipedia_tools.wiki_page
+wiki_random = _wikipedia_tools.wiki_random
+wiki_language = _wikipedia_tools.wiki_language
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("WikipediaTools Demonstration")
+    print("==================================================\n")
+    
+    # 1. Search for a topic
+    print("1. Searching Wikipedia")
+    print("------------------------------")
+    search_results = wiki_search("Python programming language")
+    print("Search results:")
+    print(json.dumps(search_results, indent=2))
+    print()
+    
+    # 2. Get a page summary
+    print("2. Getting Page Summary")
+    print("------------------------------")
+    summary = wiki_summary("Python programming language", sentences=3)
+    print("Summary:")
+    print(summary)
+    print()
+    
+    # 3. Get detailed page information
+    print("3. Getting Full Page Information")
+    print("------------------------------")
+    page = wiki_page("Python programming language")
+    if isinstance(page, dict) and "error" not in page:
+        print("Page sections:")
+        for section in page["sections"]:
+            print(f"- {section['title']} (Level {section['level']})")
+        print(f"\nNumber of references: {len(page['references'])}")
+        print(f"Number of categories: {len(page['categories'])}")
+        print(f"Number of links: {len(page['links'])}")
+        print(f"Number of images: {len(page['images'])}")
+    else:
+        print("Error getting page:", page.get("error"))
+    print()
+    
+    # 4. Get random pages
+    print("4. Getting Random Pages")
+    print("------------------------------")
+    random_pages = wiki_random(3)
+    print("Random page titles:")
+    print(json.dumps(random_pages, indent=2))
+    print()
+    
+    # 5. Try different language
+    print("5. Changing Language")
+    print("------------------------------")
+    success = wiki_language("es")
+    if success:
+        summary = wiki_summary("Python (lenguaje de programación)", sentences=1)
+        print("Spanish summary:")
+        print(summary)
+    print()
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/xml_tools.py

@@ -0,0 +1,498 @@
+"""Tools for working with XML files.
+
+Usage:
+from praisonaiagents.tools import xml_tools
+tree = xml_tools.read_xml("data.xml")
+
+or
+from praisonaiagents.tools import read_xml, write_xml, transform_xml
+tree = read_xml("data.xml")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any, Tuple
+from importlib import util
+import xml.etree.ElementTree as ET
+import xml.dom.minidom as minidom
+from io import StringIO
+import json
+
+class XMLTools:
+    """Tools for working with XML files."""
+    
+    def __init__(self):
+        """Initialize XMLTools."""
+        pass
+
+    def read_xml(
+        self,
+        filepath: str,
+        encoding: str = 'utf-8',
+        validate_schema: Optional[str] = None,
+        parser: str = 'lxml'
+    ) -> ET.Element:
+        """Read an XML file with optional schema validation.
+        
+        Args:
+            filepath: Path to XML file
+            encoding: File encoding
+            validate_schema: Optional path to XSD schema file
+            parser: XML parser to use ('lxml' or 'etree')
+            
+        Returns:
+            ElementTree root element
+        """
+        try:
+            if parser == 'lxml':
+                if util.find_spec('lxml') is None:
+                    error_msg = "lxml package is not available. Please install it using: pip install lxml"
+                    logging.error(error_msg)
+                    return None
+                import lxml.etree as lxml_etree
+                tree = lxml_etree.parse(filepath)
+                root = tree.getroot()
+            else:
+                tree = ET.parse(filepath)
+                root = tree.getroot()
+
+            if validate_schema:
+                if util.find_spec('xmlschema') is None:
+                    error_msg = "xmlschema package is not available. Please install it using: pip install xmlschema"
+                    logging.error(error_msg)
+                    return None
+                import xmlschema
+                schema = xmlschema.XMLSchema(validate_schema)
+                if not schema.is_valid(filepath):
+                    error_msg = f"XML file does not validate against schema: {schema.validate(filepath)}"
+                    logging.error(error_msg)
+                    return None
+
+            return root
+
+        except Exception as e:
+            error_msg = f"Error reading XML file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def write_xml(
+        self,
+        root: ET.Element,
+        filepath: str,
+        encoding: str = 'utf-8',
+        pretty: bool = True,
+        xml_declaration: bool = True
+    ) -> bool:
+        """Write XML Element tree to file.
+        
+        Args:
+            root: XML Element tree root
+            filepath: Output file path
+            encoding: File encoding
+            pretty: Format output with proper indentation
+            xml_declaration: Include XML declaration
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            # Convert to string
+            if pretty:
+                xml_str = minidom.parseString(
+                    ET.tostring(root, encoding='unicode')
+                ).toprettyxml(indent='  ')
+            else:
+                xml_str = ET.tostring(
+                    root,
+                    encoding='unicode'
+                )
+            
+            # Add declaration if requested
+            if xml_declaration:
+                if not xml_str.startswith('<?xml'):
+                    xml_str = (
+                        f'<?xml version="1.0" encoding="{encoding}"?>\n'
+                        + xml_str
+                    )
+            
+            # Write to file
+            with open(filepath, 'w', encoding=encoding) as f:
+                f.write(xml_str)
+            
+            return True
+        except Exception as e:
+            error_msg = f"Error writing XML file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def transform_xml(
+        self,
+        xml_file: str,
+        xslt_file: str,
+        output_file: str,
+        params: Optional[Dict[str, str]] = None
+    ) -> bool:
+        """Transform XML using XSLT stylesheet.
+        
+        Args:
+            xml_file: Input XML file
+            xslt_file: XSLT stylesheet file
+            output_file: Output file path
+            params: Optional parameters for transformation
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        try:
+            # Parse XML and XSLT
+            if util.find_spec('lxml') is None:
+                error_msg = "lxml package is not available. Please install it using: pip install lxml"
+                logging.error(error_msg)
+                return False
+            import lxml.etree as lxml_etree
+            xml_doc = lxml_etree.parse(xml_file)
+            xslt_doc = lxml_etree.parse(xslt_file)
+            transform = lxml_etree.XSLT(xslt_doc)
+            
+            # Apply transformation
+            if params:
+                result = transform(xml_doc, **params)
+            else:
+                result = transform(xml_doc)
+            
+            # Write result
+            result.write(
+                output_file,
+                pretty_print=True,
+                xml_declaration=True,
+                encoding='utf-8'
+            )
+            
+            return True
+        except Exception as e:
+            error_msg = f"Error transforming XML: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def validate_xml(
+        self,
+        xml_file: str,
+        schema_file: str
+    ) -> Tuple[bool, Optional[str]]:
+        """Validate XML against XSD schema.
+        
+        Args:
+            xml_file: XML file to validate
+            schema_file: XSD schema file
+            
+        Returns:
+            Tuple of (is_valid, error_message)
+        """
+        try:
+            if util.find_spec('xmlschema') is None:
+                error_msg = "xmlschema package is not available. Please install it using: pip install xmlschema"
+                logging.error(error_msg)
+                return False, error_msg
+            import xmlschema
+            schema = xmlschema.XMLSchema(schema_file)
+            schema.validate(xml_file)
+            return True, None
+        except xmlschema.validators.exceptions.XMLSchemaValidationError as e:
+            return False, str(e)
+        except Exception as e:
+            error_msg = f"Error validating XML: {str(e)}"
+            logging.error(error_msg)
+            return False, error_msg
+
+    def xml_to_dict(
+        self,
+        root: Union[str, ET.Element],
+        preserve_attrs: bool = True
+    ) -> Dict[str, Any]:
+        """Convert XML to dictionary.
+        
+        Args:
+            root: XML string or Element tree root
+            preserve_attrs: Keep XML attributes in result
+            
+        Returns:
+            Dict representation of XML
+        """
+        try:
+            # Parse XML if string
+            if isinstance(root, str):
+                if root.startswith('<'):
+                    root = ET.fromstring(root)
+                else:
+                    root = ET.parse(root).getroot()
+            
+            result = {}
+            
+            # Add attributes if present and requested
+            if preserve_attrs and root.attrib:
+                result['@attributes'] = dict(root.attrib)
+            
+            # Add children
+            children = list(root)
+            if not children:
+                text = root.text
+                if text is not None and text.strip():
+                    result = text.strip()
+            else:
+                for child in children:
+                    child_data = self.xml_to_dict(child, preserve_attrs)
+                    if child.tag in result:
+                        if not isinstance(result[child.tag], list):
+                            result[child.tag] = [result[child.tag]]
+                        result[child.tag].append(child_data)
+                    else:
+                        result[child.tag] = child_data
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error converting XML to dict: {str(e)}"
+            logging.error(error_msg)
+            return {}
+
+    def dict_to_xml(
+        self,
+        data: Dict[str, Any],
+        root_tag: str = 'root'
+    ) -> Optional[ET.Element]:
+        """Convert dictionary to XML.
+        
+        Args:
+            data: Dictionary to convert
+            root_tag: Tag for root element
+            
+        Returns:
+            XML Element tree root
+        """
+        try:
+            def _create_element(
+                parent: ET.Element,
+                key: str,
+                value: Any
+            ):
+                """Create XML element from key-value pair."""
+                if key == '@attributes':
+                    for attr_key, attr_val in value.items():
+                        parent.set(attr_key, str(attr_val))
+                elif isinstance(value, dict):
+                    child = ET.SubElement(parent, key)
+                    for k, v in value.items():
+                        _create_element(child, k, v)
+                elif isinstance(value, list):
+                    for item in value:
+                        child = ET.SubElement(parent, key)
+                        if isinstance(item, dict):
+                            for k, v in item.items():
+                                _create_element(child, k, v)
+                        else:
+                            child.text = str(item)
+                else:
+                    child = ET.SubElement(parent, key)
+                    child.text = str(value)
+            
+            root = ET.Element(root_tag)
+            for key, value in data.items():
+                _create_element(root, key, value)
+            
+            return root
+        except Exception as e:
+            error_msg = f"Error converting dict to XML: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def xpath_query(
+        self,
+        root: Union[str, ET.Element],
+        query: str,
+        namespaces: Optional[Dict[str, str]] = None
+    ) -> List[ET.Element]:
+        """Execute XPath query on XML.
+        
+        Args:
+            root: XML string or Element tree root
+            query: XPath query string
+            namespaces: Optional namespace mappings
+            
+        Returns:
+            List of matching elements
+        """
+        try:
+            # Parse XML if string
+            if isinstance(root, str):
+                if root.startswith('<'):
+                    if util.find_spec('lxml') is None:
+                        error_msg = "lxml package is not available. Please install it using: pip install lxml"
+                        logging.error(error_msg)
+                        return []
+                    import lxml.etree as lxml_etree
+                    tree = lxml_etree.fromstring(root)
+                else:
+                    tree = ET.parse(root)
+            else:
+                if util.find_spec('lxml') is None:
+                    error_msg = "lxml package is not available. Please install it using: pip install lxml"
+                    logging.error(error_msg)
+                    return []
+                import lxml.etree as lxml_etree
+                tree = lxml_etree.fromstring(
+                    ET.tostring(root, encoding='unicode')
+                )
+            
+            # Execute query
+            results = tree.xpath(
+                query,
+                namespaces=namespaces or {}
+            )
+            
+            # Convert results to standard ElementTree elements
+            return [
+                ET.fromstring(lxml_etree.tostring(elem, encoding='unicode'))
+                for elem in results
+            ]
+        except Exception as e:
+            error_msg = f"Error executing XPath query: {str(e)}"
+            logging.error(error_msg)
+            return []
+
+# Create instance for direct function access
+_xml_tools = XMLTools()
+read_xml = _xml_tools.read_xml
+write_xml = _xml_tools.write_xml
+transform_xml = _xml_tools.transform_xml
+validate_xml = _xml_tools.validate_xml
+xml_to_dict = _xml_tools.xml_to_dict
+dict_to_xml = _xml_tools.dict_to_xml
+xpath_query = _xml_tools.xpath_query
+
+if __name__ == "__main__":
+    print("\n==================================================")
+    print("XMLTools Demonstration")
+    print("==================================================\n")
+
+    # Create temporary files
+    import tempfile
+    import os
+
+    temp_file = tempfile.mktemp(suffix='.xml')
+    try:
+        print("1. Creating XML Document")
+        print("------------------------------")
+        xml_content = """<?xml version="1.0" encoding="UTF-8"?>
+<bookstore>
+    <book category="fiction">
+        <title>The Great Gatsby</title>
+        <author>F. Scott Fitzgerald</author>
+        <year>1925</year>
+        <price>10.99</price>
+    </book>
+    <book category="non-fiction">
+        <title>A Brief History of Time</title>
+        <author>Stephen Hawking</author>
+        <year>1988</year>
+        <price>15.99</price>
+    </book>
+</bookstore>"""
+        
+        with open(temp_file, 'w') as f:
+            f.write(xml_content)
+        print("Sample XML file created")
+        print()
+
+        print("2. Parsing XML")
+        print("------------------------------")
+        result = read_xml(temp_file)
+        print("XML structure:")
+        print(minidom.parseString(ET.tostring(result, encoding='unicode')).toprettyxml(indent='  '))
+        print()
+
+        print("3. Querying XML")
+        print("------------------------------")
+        xpath = "//book[@category='fiction']/title/text()"
+        result = xpath_query(result, xpath)
+        print(f"Fiction book titles (XPath: {xpath}):")
+        for title in result:
+            print(title.text)
+        print()
+
+        print("4. Validating XML")
+        print("------------------------------")
+        schema_file = tempfile.mktemp(suffix='.xsd')
+        schema_content = """<?xml version="1.0" encoding="UTF-8"?>
+<xs:schema xmlns:xs="http://www.w3.org/2001/XMLSchema">
+    <xs:element name="bookstore">
+        <xs:complexType>
+            <xs:sequence>
+                <xs:element name="book" maxOccurs="unbounded">
+                    <xs:complexType>
+                        <xs:sequence>
+                            <xs:element name="title" type="xs:string"/>
+                            <xs:element name="author" type="xs:string"/>
+                            <xs:element name="year" type="xs:integer"/>
+                            <xs:element name="price" type="xs:decimal"/>
+                        </xs:sequence>
+                        <xs:attribute name="category" type="xs:string"/>
+                    </xs:complexType>
+                </xs:element>
+            </xs:sequence>
+        </xs:complexType>
+    </xs:element>
+</xs:schema>"""
+        with open(schema_file, 'w') as f:
+            f.write(schema_content)
+        result, error = validate_xml(temp_file, schema_file)
+        print(f"XML validation result: {result}")
+        if error:
+            print(f"Error: {error}")
+        print()
+
+        print("5. Transforming XML")
+        print("------------------------------")
+        xslt_content = """<?xml version="1.0" encoding="UTF-8"?>
+<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
+    <xsl:template match="/">
+        <html>
+            <body>
+                <h2>Bookstore Inventory</h2>
+                <table>
+                    <tr>
+                        <th>Title</th>
+                        <th>Author</th>
+                        <th>Price</th>
+                    </tr>
+                    <xsl:for-each select="bookstore/book">
+                        <tr>
+                            <td><xsl:value-of select="title"/></td>
+                            <td><xsl:value-of select="author"/></td>
+                            <td><xsl:value-of select="price"/></td>
+                        </tr>
+                    </xsl:for-each>
+                </table>
+            </body>
+        </html>
+    </xsl:template>
+</xsl:stylesheet>"""
+
+        xslt_file = tempfile.mktemp(suffix='.xslt')
+        with open(xslt_file, 'w') as f:
+            f.write(xslt_content)
+
+        output_file = tempfile.mktemp(suffix='.html')
+        result = transform_xml(temp_file, xslt_file, output_file)
+        print(f"XML transformation result: {result}")
+        if result and os.path.exists(output_file):
+            print("\nTransformed HTML content:")
+            with open(output_file, 'r') as f:
+                print(f.read())
+
+    finally:
+        # Clean up temporary files
+        for file in [temp_file, schema_file, xslt_file, output_file]:
+            if os.path.exists(file):
+                os.unlink(file)
+
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")

agents/praisonaiagents/tools/yaml_tools.py

@@ -0,0 +1,417 @@
+"""Tools for working with YAML files.
+
+Usage:
+from praisonaiagents.tools import yaml_tools
+data = yaml_tools.read_yaml("config.yaml")
+
+or
+from praisonaiagents.tools import read_yaml, write_yaml, merge_yaml
+data = read_yaml("config.yaml")
+"""
+
+import logging
+from typing import List, Dict, Union, Optional, Any
+from importlib import util
+import os
+from copy import deepcopy
+
+class YAMLTools:
+    """Tools for working with YAML files."""
+    
+    def __init__(self):
+        """Initialize YAMLTools."""
+        self._check_dependencies()
+        
+    def _check_dependencies(self):
+        """Check if required packages are installed."""
+        missing = []
+        for package, module in [('pyyaml', 'yaml'), ('jsonschema', 'jsonschema')]:
+            if util.find_spec(module) is None:
+                missing.append(package)
+        
+        if missing:
+            raise ImportError(
+                f"Required packages not available. Please install: {', '.join(missing)}\n"
+                f"Run: pip install {' '.join(missing)}"
+            )
+
+    def read_yaml(
+        self,
+        filepath: str,
+        safe_load: bool = True,
+        encoding: str = 'utf-8'
+    ) -> Optional[Union[Dict[str, Any], List[Any]]]:
+        """Read a YAML file."""
+        try:
+            if util.find_spec('yaml') is None:
+                error_msg = "pyyaml package is not available. Please install it using: pip install pyyaml"
+                logging.error(error_msg)
+                return None
+
+            # Import yaml only when needed
+            import yaml
+            
+            with open(filepath, 'r', encoding=encoding) as f:
+                if safe_load:
+                    return yaml.safe_load(f)
+                return yaml.load(f, Loader=yaml.FullLoader)
+        except Exception as e:
+            error_msg = f"Error reading YAML file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def write_yaml(
+        self,
+        data: Union[Dict[str, Any], List[Any]],
+        filepath: str,
+        default_flow_style: bool = False,
+        encoding: str = 'utf-8',
+        sort_keys: bool = False,
+        allow_unicode: bool = True
+    ) -> bool:
+        """Write data to YAML file."""
+        try:
+            if util.find_spec('yaml') is None:
+                error_msg = "pyyaml package is not available. Please install it using: pip install pyyaml"
+                logging.error(error_msg)
+                return False
+
+            # Import yaml only when needed
+            import yaml
+            
+            with open(filepath, 'w', encoding=encoding) as f:
+                yaml.dump(
+                    data,
+                    f,
+                    default_flow_style=default_flow_style,
+                    sort_keys=sort_keys,
+                    allow_unicode=allow_unicode
+                )
+            return True
+        except Exception as e:
+            error_msg = f"Error writing YAML file {filepath}: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def merge_yaml(
+        self,
+        files: List[str],
+        output_file: Optional[str] = None,
+        strategy: str = 'deep'
+    ) -> Union[Dict[str, Any], List[Any], None]:
+        """Merge multiple YAML files."""
+        try:
+            if util.find_spec('yaml') is None:
+                error_msg = "pyyaml package is not available. Please install it using: pip install pyyaml"
+                logging.error(error_msg)
+                return None
+
+            def deep_merge(source: dict, destination: dict) -> dict:
+                """Deep merge two dictionaries."""
+                for key, value in source.items():
+                    if key in destination:
+                        if isinstance(value, dict) and isinstance(destination[key], dict):
+                            destination[key] = deep_merge(value, destination[key])
+                        elif isinstance(value, list) and isinstance(destination[key], list):
+                            destination[key].extend(value)
+                        else:
+                            destination[key] = value
+                    else:
+                        destination[key] = value
+                return destination
+            
+            result = {}
+            for file in files:
+                data = self.read_yaml(file)
+                if data is None:
+                    continue
+                
+                if strategy == 'deep':
+                    if isinstance(data, dict):
+                        result = deep_merge(data, result)
+                    elif isinstance(data, list):
+                        if not result:
+                            result = []
+                        result.extend(data)
+                else:  # shallow
+                    if isinstance(data, dict):
+                        result.update(data)
+                    elif isinstance(data, list):
+                        if not result:
+                            result = []
+                        result.extend(data)
+            
+            if output_file:
+                self.write_yaml(result, output_file)
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error merging YAML files: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def validate_yaml(
+        self,
+        data: Union[Dict[str, Any], List[Any], str],
+        schema: Dict[str, Any]
+    ) -> bool:
+        """Validate YAML data against a schema."""
+        try:
+            if util.find_spec('jsonschema') is None:
+                error_msg = "jsonschema package is not available. Please install it using: pip install jsonschema"
+                logging.error(error_msg)
+                return False
+
+            # Import jsonschema only when needed
+            from jsonschema import validate, ValidationError
+            
+            # Load data if file path
+            if isinstance(data, str):
+                data = self.read_yaml(data)
+                if data is None:
+                    return False
+            
+            try:
+                validate(instance=data, schema=schema)
+                return True
+            except ValidationError as e:
+                logging.error(f"YAML validation error: {str(e)}")
+                return False
+        except Exception as e:
+            error_msg = f"Error validating YAML: {str(e)}"
+            logging.error(error_msg)
+            return False
+
+    def analyze_yaml(
+        self,
+        data: Union[Dict[str, Any], List[Any], str]
+    ) -> Optional[Dict[str, Any]]:
+        """Analyze YAML data structure."""
+        try:
+            # Load data if file path
+            if isinstance(data, str):
+                data = self.read_yaml(data)
+                if data is None:
+                    return None
+            
+            def analyze_value(value: Any) -> Dict[str, Any]:
+                """Analyze a single value."""
+                result = {
+                    'type': type(value).__name__,
+                    'size': len(value) if hasattr(value, '__len__') else 1
+                }
+                
+                if isinstance(value, dict):
+                    result['keys'] = list(value.keys())
+                    result['nested_types'] = {
+                        k: type(v).__name__
+                        for k, v in value.items()
+                    }
+                elif isinstance(value, list):
+                    result['element_types'] = list(set(
+                        type(x).__name__ for x in value
+                    ))
+                
+                return result
+            
+            result = {
+                'structure': analyze_value(data),
+                'stats': {
+                    'total_keys': sum(1 for _ in self._walk_dict(data))
+                    if isinstance(data, dict) else len(data)
+                }
+            }
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error analyzing YAML: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+    def _walk_dict(self, d: Dict[str, Any]) -> Any:
+        """Walk through nested dictionary."""
+        for k, v in d.items():
+            yield k
+            if isinstance(v, dict):
+                yield from self._walk_dict(v)
+
+    def transform_yaml(
+        self,
+        data: Union[Dict[str, Any], List[Any], str],
+        operations: List[Dict[str, Any]]
+    ) -> Optional[Union[Dict[str, Any], List[Any]]]:
+        """Transform YAML data using specified operations."""
+        try:
+            # Load data if file path
+            if isinstance(data, str):
+                data = self.read_yaml(data)
+                if data is None:
+                    return None
+            
+            result = deepcopy(data)
+            
+            for op in operations:
+                op_type = op.get('type')
+                path = op.get('path', '')
+                value = op.get('value')
+                
+                if not op_type:
+                    continue
+                
+                # Split path into parts
+                parts = [p for p in path.split('/') if p]
+                
+                # Get reference to target location
+                target = result
+                parent = None
+                last_key = None
+                
+                for part in parts[:-1]:
+                    if isinstance(target, dict):
+                        if part not in target:
+                            target[part] = {}
+                        parent = target
+                        target = target[part]
+                        last_key = part
+                    elif isinstance(target, list):
+                        idx = int(part)
+                        while len(target) <= idx:
+                            target.append({})
+                        parent = target
+                        target = target[idx]
+                        last_key = idx
+                
+                if parts:
+                    last_part = parts[-1]
+                    if isinstance(target, dict):
+                        if op_type == 'set':
+                            target[last_part] = value
+                        elif op_type == 'delete':
+                            target.pop(last_part, None)
+                        elif op_type == 'append' and isinstance(target.get(last_part), list):
+                            target[last_part].append(value)
+                    elif isinstance(target, list):
+                        idx = int(last_part)
+                        if op_type == 'set':
+                            while len(target) <= idx:
+                                target.append(None)
+                            target[idx] = value
+                        elif op_type == 'delete' and idx < len(target):
+                            del target[idx]
+                        elif op_type == 'append':
+                            target.append(value)
+                else:
+                    if op_type == 'set':
+                        result = value
+                    elif op_type == 'append' and isinstance(result, list):
+                        result.append(value)
+            
+            return result
+        except Exception as e:
+            error_msg = f"Error transforming YAML: {str(e)}"
+            logging.error(error_msg)
+            return None
+
+# Create instance for direct function access
+_yaml_tools = YAMLTools()
+read_yaml = _yaml_tools.read_yaml
+write_yaml = _yaml_tools.write_yaml
+merge_yaml = _yaml_tools.merge_yaml
+validate_yaml = _yaml_tools.validate_yaml
+analyze_yaml = _yaml_tools.analyze_yaml
+transform_yaml = _yaml_tools.transform_yaml
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("YAMLTools Demonstration")
+    print("==================================================\n")
+    
+    # Sample data
+    data1 = {
+        'server': {
+            'host': 'localhost',
+            'port': 8080,
+            'debug': True
+        },
+        'database': {
+            'url': 'postgresql://localhost:5432/db',
+            'pool_size': 5
+        }
+    }
+    
+    data2 = {
+        'server': {
+            'workers': 4,
+            'timeout': 30
+        },
+        'logging': {
+            'level': 'INFO',
+            'file': 'app.log'
+        }
+    }
+    
+    # 1. Write YAML files
+    print("1. Writing YAML Files")
+    print("------------------------------")
+    success1 = write_yaml(data1, 'config1.yaml')
+    success2 = write_yaml(data2, 'config2.yaml')
+    print(f"First file written: {success1}")
+    print(f"Second file written: {success2}")
+    print()
+    
+    # 2. Read YAML file
+    print("2. Reading YAML File")
+    print("------------------------------")
+    config = read_yaml('config1.yaml')
+    print("YAML content:")
+    print(config)
+    print()
+    
+    # 3. Merge YAML files
+    print("3. Merging YAML Files")
+    print("------------------------------")
+    merged = merge_yaml(['config1.yaml', 'config2.yaml'])
+    print("Merged content:")
+    print(merged)
+    print()
+    
+    # 4. Analyze YAML
+    print("4. Analyzing YAML")
+    print("------------------------------")
+    analysis = analyze_yaml(merged)
+    print("Analysis results:")
+    print(analysis)
+    print()
+    
+    # 5. Transform YAML
+    print("5. Transforming YAML")
+    print("------------------------------")
+    operations = [
+        {
+            'type': 'set',
+            'path': 'server/host',
+            'value': '0.0.0.0'
+        },
+        {
+            'type': 'delete',
+            'path': 'server/debug'
+        },
+        {
+            'type': 'set',
+            'path': 'logging/handlers',
+            'value': ['console', 'file']
+        }
+    ]
+    transformed = transform_yaml(merged, operations)
+    print("Transformed content:")
+    print(transformed)
+    
+    print("\n==================================================")
+    print("Demonstration Complete")
+    print("==================================================")
+    
+    # Cleanup
+    for file in ['config1.yaml', 'config2.yaml']:
+        if os.path.exists(file):
+            os.remove(file)

agents/praisonaiagents/tools/yfinance_tools.py

@@ -0,0 +1,213 @@
+"""YFinance tools for stock market data.
+
+Usage:
+from praisonaiagents.tools import get_stock_price, get_stock_info
+price = get_stock_price("AAPL")
+info = get_stock_info("AAPL")
+
+or 
+from praisonaiagents.tools import yfinance
+price = yfinance.get_stock_price("AAPL")
+info = yfinance.get_stock_info("AAPL")
+"""
+
+from typing import List, Dict, Optional, Any
+import logging
+from importlib import util
+from datetime import datetime
+
+class YFinanceTools:
+    """A comprehensive tool for financial data analysis using yfinance"""
+
+    def __init__(self):
+        """Initialize YFinanceTools"""
+        self._tickers = {}
+
+    def _get_yfinance(self):
+        """Get yfinance module, installing if needed"""
+        if util.find_spec('yfinance') is None:
+            error_msg = "yfinance package is not available. Please install it using: pip install yfinance"
+            logging.error(error_msg)
+            return None
+        import yfinance as yf
+        return yf
+
+    def _get_ticker(self, symbol: str):
+        """Get or create ticker instance"""
+        if symbol not in self._tickers:
+            yf = self._get_yfinance()
+            if yf is None:
+                return None
+            self._tickers[symbol] = yf.Ticker(symbol)
+        return self._tickers[symbol]
+
+    def get_stock_price(self, symbol: str) -> Dict[str, float]:
+        """
+        Get current stock price and related price metrics
+        
+        Args:
+            symbol (str): Stock ticker symbol
+            
+        Returns:
+            Dict[str, float]: Current price information
+        """
+        try:
+            ticker = self._get_ticker(symbol)
+            if ticker is None:
+                return {"error": "yfinance package not available"}
+
+            info = ticker.info
+            return {
+                'symbol': symbol,
+                'price': info.get('regularMarketPrice', 0.0),
+                'open': info.get('regularMarketOpen', 0.0),
+                'high': info.get('regularMarketDayHigh', 0.0),
+                'low': info.get('regularMarketDayLow', 0.0),
+                'volume': info.get('regularMarketVolume', 0),
+                'previous_close': info.get('regularMarketPreviousClose', 0.0),
+                'change': info.get('regularMarketChange', 0.0),
+                'change_percent': info.get('regularMarketChangePercent', 0.0)
+            }
+        except Exception as e:
+            error_msg = f"Error getting stock price for {symbol}: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_stock_info(self, symbol: str) -> Dict:
+        """
+        Get detailed information about a stock
+        
+        Args:
+            symbol (str): Stock ticker symbol
+            
+        Returns:
+            Dict: Stock information including company details and key metrics
+        """
+        try:
+            ticker = self._get_ticker(symbol)
+            if ticker is None:
+                return {"error": "yfinance package not available"}
+
+            info = ticker.info
+            
+            relevant_info = {
+                "longName": info.get("longName"),
+                "symbol": info.get("symbol"),
+                "sector": info.get("sector"), 
+                "industry": info.get("industry"),
+                "country": info.get("country"),
+                "marketCap": info.get("marketCap"),
+                "currentPrice": info.get("currentPrice"),
+                "currency": info.get("currency"),
+                "exchange": info.get("exchange"),
+                "fiftyTwoWeekHigh": info.get("fiftyTwoWeekHigh"),
+                "fiftyTwoWeekLow": info.get("fiftyTwoWeekLow"),
+                "trailingPE": info.get("trailingPE"),
+                "forwardPE": info.get("forwardPE"),
+                "dividendYield": info.get("dividendYield"),
+                "beta": info.get("beta"),
+                "volume": info.get("volume"),
+                "averageVolume": info.get("averageVolume"),
+                "sharesOutstanding": info.get("sharesOutstanding"),
+                "website": info.get("website"),
+                "longBusinessSummary": info.get("longBusinessSummary")
+            }
+            return {k: v for k, v in relevant_info.items() if v is not None}
+        except Exception as e:
+            error_msg = f"Error fetching stock info: {str(e)}"
+            logging.error(error_msg)
+            return {"error": error_msg}
+
+    def get_historical_data(
+        self, 
+        symbol: str, 
+        period: str = "1y",
+        interval: str = "1d",
+        start: Optional[datetime] = None,
+        end: Optional[datetime] = None
+    ) -> List[Dict[str, Any]]:
+        """
+        Get historical price data for a stock
+        
+        Args:
+            symbol (str): Stock ticker symbol
+            period (str): Data period e.g. 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
+            interval (str): Data interval e.g. 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo
+            start (datetime, optional): Start date for data
+            end (datetime, optional): End date for data
+            
+        Returns:
+            List[Dict[str, Any]]: List of historical price data points
+        """
+        try:
+            ticker = self._get_ticker(symbol)
+            if ticker is None:
+                return [{"error": "yfinance package not available"}]
+
+            hist = ticker.history(period=period, interval=interval, start=start, end=end)
+            return hist.reset_index().to_dict('records')
+        except Exception as e:
+            error_msg = f"Error fetching historical data: {str(e)}"
+            logging.error(error_msg)
+            return [{"error": error_msg}]
+
+if __name__ == "__main__":
+    # Example usage
+    print("\n==================================================")
+    print("YFinanceTools Demonstration")
+    print("==================================================\n")
+
+    try:
+        yf_tools = YFinanceTools()
+
+        # 1. Basic Stock Information
+        print("1. Basic Stock Information for AAPL")
+        print("------------------------------")
+        info = yf_tools.get_stock_info("AAPL")
+        if "error" not in info:
+            print(f"Company: {info.get('longName')}")
+            print(f"Sector: {info.get('sector')}")
+            print(f"Industry: {info.get('industry')}")
+            print(f"Country: {info.get('country')}\n")
+        else:
+            print(f"Error: {info['error']}\n")
+
+        # 2. Current Price Data
+        print("2. Current Price Data")
+        print("------------------------------")
+        price_data = yf_tools.get_stock_price("AAPL")
+        if "error" not in price_data:
+            print(f"Current Price: ${price_data.get('price', 'N/A')}")
+            print(f"Previous Close: ${price_data.get('previous_close', 'N/A')}")
+            print(f"Day Range: ${price_data.get('low', 'N/A')} - ${price_data.get('high', 'N/A')}\n")
+        else:
+            print(f"Error: {price_data['error']}\n")
+
+        # 3. Historical Data
+        print("3. Historical Data (Last 5 Days)")
+        print("------------------------------")
+        hist_data = yf_tools.get_historical_data("AAPL", period="5d")
+        if not any("error" in d for d in hist_data):
+            for day in hist_data:
+                print(f"Date: {day.get('Date')}, Close: ${day.get('Close', 'N/A'):.2f}")
+            print()
+        else:
+            print(f"{hist_data[0]['error']}\n")
+
+        # 4. Multi-Stock Example
+        print("5. Multi-Stock Current Prices")
+        print("------------------------------")
+        for symbol in ["AAPL", "MSFT", "GOOGL"]:
+            price_data = yf_tools.get_stock_price(symbol)
+            if "error" not in price_data:
+                print(f"{symbol}: ${price_data.get('price', 'N/A')}")
+            else:
+                print(f"{symbol}: Error fetching price")
+        print()
+
+    except Exception as e:
+        print(f"Error: {str(e)}")
+
+    print("==================================================")
+    print("Analysis Complete")
+    print("==================================================")

agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.4"
+version = "0.0.30"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }
@@ -13,4 +13,9 @@ dependencies = [
     "pydantic",
     "rich",
     "openai"
+]
+
+[project.optional-dependencies]
+memory = [
+    "chromadb>=0.6.0"
 ] 

agents/structured_agents_example.py

@@ -0,0 +1,56 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents, Tools
+from pydantic import BaseModel
+
+class AnalysisReport(BaseModel):
+    title: str
+    findings: str
+    summary: str
+
+# Create a researcher agent
+researcher = Agent(
+    name="AIResearcher",
+    role="Technology Research Analyst",
+    goal="Analyze and structure information about AI developments",
+    backstory="Expert analyst specializing in AI technology trends",
+    verbose=True,
+    llm="gpt-4o-mini",
+    tools=[Tools.internet_search],
+    self_reflect=False
+)
+
+# Create an analyst agent
+analyst = Agent(
+    name="DataAnalyst",
+    role="Data Insights Specialist",
+    goal="Structure and analyze research findings",
+    backstory="Senior data analyst with expertise in pattern recognition",
+    verbose=True,
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# Define structured tasks
+research_task = Task(
+    name="gather_research",
+    description="Research recent AI developments in 2024",
+    agent=researcher,
+    expected_output="Research findings"
+)
+
+analysis_task = Task(
+    name="analyze_findings",
+    description="Analyze research findings and create structured report. No additional text or explanation.",
+    agent=analyst,
+    output_json=AnalysisReport,
+    expected_output="JSON object"
+)
+
+# Initialize and run agents
+agents = PraisonAIAgents(
+    agents=[researcher, analyst],
+    tasks=[research_task, analysis_task],
+    process="sequential",
+    verbose=True
+)
+result = agents.start()
+print(result)

agents/structured_response_example.py

@@ -0,0 +1,95 @@
+from praisonaiagents import Agent
+from pydantic import BaseModel
+from typing import List, Optional
+
+# Define structured output models
+class SearchResult(BaseModel):
+    title: str
+    url: Optional[str]
+    snippet: str
+
+class AnalysisReport(BaseModel):
+    topic: str
+    key_findings: List[str]
+    search_results: List[SearchResult]
+    summary: str
+    confidence_score: float
+
+def get_structured_analysis(query: str, verbose: bool = True) -> AnalysisReport:
+    """
+    Performs a search and returns a structured analysis using an AI agent.
+    
+    Args:
+        query (str): The search query or topic to analyze
+        verbose (bool): Whether to show detailed output
+        
+    Returns:
+        AnalysisReport: A structured report containing the analysis
+    """
+    # Create an agent with search capabilities
+    agent = Agent(
+        name="StructuredAnalyst",
+        role="Research Analyst",
+        goal="Analyze topics and provide structured reports",
+        backstory="Expert at gathering information and providing structured analysis",
+        verbose=verbose,
+        self_reflect=True,  # Enable self-reflection for better quality
+        markdown=True,
+        llm="gpt-4o-mini"
+    )
+    
+    # Create the analysis prompt
+    prompt = f"""
+Analyze the following topic: "{query}"
+
+Provide a structured analysis in JSON format that matches this schema:
+{{
+    "topic": "string",
+    "key_findings": ["string"],
+    "search_results": [
+        {{
+            "title": "string",
+            "url": "string",
+            "snippet": "string"
+        }}
+    ],
+    "summary": "string",
+    "confidence_score": float (0-1)
+}}
+
+Requirements:
+1. Include at least 3 key findings
+2. Include at least 2 relevant search results
+3. Provide a comprehensive summary
+4. Set confidence score based on quality of sources (0-1)
+
+Return ONLY the JSON object, no other text.
+"""
+    
+    # Get structured response from agent
+    response = agent.chat(
+        prompt=prompt,
+        output_json=AnalysisReport  # This ensures response matches our model
+    )
+    
+    return AnalysisReport.model_validate_json(response)
+
+if __name__ == "__main__":
+    # Example usage
+    analysis = get_structured_analysis("Latest developments in AI agents and autonomous systems")
+    print(analysis)
+    # Print the structured results
+    print("\n=== Structured Analysis Report ===")
+    print(f"Topic: {analysis.topic}")
+    print("\nKey Findings:")
+    for i, finding in enumerate(analysis.key_findings, 1):
+        print(f"{i}. {finding}")
+    
+    print("\nSearch Results:")
+    for result in analysis.search_results:
+        print(f"\nTitle: {result.title}")
+        print(f"URL: {result.url}")
+        print(f"Snippet: {result.snippet}")
+    
+    print(f"\nSummary: {analysis.summary}")
+    print(f"Confidence Score: {analysis.confidence_score:.2f}") 

agents/test.py

@@ -0,0 +1,147 @@
+from typing import List, Dict
+from pydantic import BaseModel
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.main import TaskOutput
+
+# Pydantic models for structured output
+class Chapter(BaseModel):
+    chapter_number: int
+    title: str
+    outline: str
+
+class StoryOutline(BaseModel):
+    title: str
+    total_chapters: int
+    chapters: List[Chapter]
+
+class ChapterContent(BaseModel):
+    chapter_number: int
+    title: str
+    content: str
+
+# Create the story planner agent
+planner_agent = Agent(
+    name="Story Planner",
+    role="Creative Writing Architect",
+    goal="Create detailed story outlines with chapter breakdowns",
+    backstory="Expert storyteller skilled in narrative structure and plot development",
+    verbose=True,
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# Create the writer agent
+writer_agent = Agent(
+    name="Story Writer",
+    role="Creative Writer",
+    goal="Write engaging and cohesive story chapters",
+    backstory="Experienced writer skilled in bringing stories to life with vivid details and engaging narrative",
+    verbose=True,
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# Story title
+story_title = "The Curious Cat"
+
+# Step 1: Create outline task
+outline_task = Task(
+    name="story_outline",
+    description=f"""Create a detailed story outline for a story titled '{story_title}'.
+    The outline should include:
+    1. A compelling story arc
+    2. Clear chapter breakdown
+    3. Brief description of what happens in each chapter
+    4. Character introductions and development points
+    
+    Provide the response in this exact JSON format:
+    {{
+        "title": "Story title",
+        "total_chapters": number,
+        "chapters": [
+            {{
+                "chapter_number": 1,
+                "title": "Chapter title",
+                "outline": "Detailed chapter outline"
+            }}
+        ]
+    }}
+    """,
+    expected_output="Complete story outline",
+    agent=planner_agent,
+    output_pydantic=StoryOutline
+)
+
+# Create initial workflow for outline
+outline_workflow = PraisonAIAgents(
+    agents=[planner_agent],
+    tasks=[outline_task],
+    verbose=True,
+    process="sequential"
+)
+
+# Get the outline
+outline_result = outline_workflow.start()
+story_outline = outline_result["task_results"][0].pydantic
+
+# Step 2: Create tasks for each chapter
+chapter_tasks = []
+chapters_content = []
+
+# Create a task for each chapter
+for chapter in story_outline.chapters:
+    chapter_task = Task(
+        name=f"write_chapter_{chapter.chapter_number}",
+        description=f"""Write chapter {chapter.chapter_number}: {chapter.title}
+        Chapter outline: {chapter.outline}
+        
+        Write a complete, engaging chapter following this outline.
+        Return in this exact JSON format:
+        {{
+            "chapter_number": {chapter.chapter_number},
+            "title": "{chapter.title}",
+            "content": "The full chapter content..."
+        }}
+        """,
+        expected_output="Complete chapter content",
+        agent=writer_agent,
+        output_pydantic=ChapterContent,
+        context=[outline_task]
+    )
+    chapter_tasks.append(chapter_task)
+
+# Create sequential workflow for chapters
+chapter_workflow = PraisonAIAgents(
+    agents=[writer_agent],
+    tasks=chapter_tasks,
+    verbose=True,
+    process="sequential"
+)
+
+# Run the chapter workflow
+chapter_results = chapter_workflow.start()
+
+# Collect and validate all chapter contents
+for task_result in chapter_results["task_results"].values():
+    if task_result and task_result.pydantic:
+        chapters_content.append(task_result.pydantic)
+
+# Sort chapters by chapter number
+chapters_content.sort(key=lambda x: x.chapter_number)
+
+# Format final result
+story = {
+    "title": story_outline.title,
+    "outline": story_outline.model_dump(),
+    "chapters": [chapter.model_dump() for chapter in chapters_content]
+}
+
+# Print results
+print("\n=== Story Generation Results ===")
+print(f"\nTitle: {story['title']}")
+print("\nOutline:")
+print(story['outline'])
+print("\nChapters:")
+for chapter in story['chapters']:
+    print(f"\nChapter {chapter['chapter_number']}: {chapter['title']}")
+    print(chapter['content'][:200] + "...")  # Print first 200 chars of each chapter

agents/tools_example.py

@@ -0,0 +1,30 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import get_article, get_news_sources, get_articles_from_source, get_trending_topics
+
+# Create Wikipedia agent
+news_agent = Agent(
+    name="NewsAgent",
+    role="News Analyst",
+    goal="Collect and analyze news articles from various sources.",
+    backstory="Expert in news gathering and content analysis.",
+    tools=[get_article, get_news_sources, get_articles_from_source, get_trending_topics],
+    self_reflect=False
+)
+
+
+# Define research task
+news_task = Task(
+    description="Analyze news articles about 'AI developments' from major tech news sources.",
+    expected_output="Summary of key AI developments with source articles.",
+    agent=news_agent,
+    name="ai_news"
+)
+
+
+# Run agent
+agents = PraisonAIAgents(
+    agents=[news_agent],
+    tasks=[news_task],
+    process="sequential"
+)
+agents.start()

agents/workflow_example_basic.py

@@ -0,0 +1,74 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from typing import List, Dict
+from duckduckgo_search import DDGS
+
+# 1. Tool
+def internet_search_tool(query: str) -> List[Dict]:
+    """
+    Perform a search using DuckDuckGo.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        list: A list of search result titles, URLs, and snippets.
+    """
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=10):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", ""),
+                "snippet": result.get("body", "")
+            })
+        return results
+
+    except Exception as e:
+        print(f"Error during DuckDuckGo search: {e}")
+        return []
+
+# 2. Agent
+data_agent = Agent(
+    name="DataCollector",
+    role="Search Specialist",
+    goal="Perform internet searches to collect relevant information.",
+    backstory="Expert in finding and organising internet data.",
+    tools=[internet_search_tool],
+    self_reflect=False
+)
+
+# 3. Tasks
+collect_task = Task(
+    description="Perform an internet search using the query: 'AI job trends in 2024'. Return results as a list of title, URL, and snippet.",
+    expected_output="List of search results with titles, URLs, and snippets.",
+    agent=data_agent,
+    name="collect_data",
+    is_start=True,
+    next_tasks=["validate_data"]
+)
+
+validate_task = Task(
+    description="""Validate the collected data. Check if:
+    1. At least 5 results are returned.
+    2. Each result contains a title and a URL.
+    Return validation_result as 'valid' or 'invalid' only no other text.""",
+    expected_output="Validation result indicating if data is valid or invalid.",
+    agent=data_agent,
+    name="validate_data",
+    task_type="decision",
+    condition={
+        "valid": [],  # End the workflow on valid data
+        "invalid": ["collect_data"]  # Retry data collection on invalid data
+    },
+)
+
+# 4. AI Agents Workflow
+agents = PraisonAIAgents(
+    agents=[data_agent],
+    tasks=[collect_task, validate_task],
+    verbose=1,
+    process="workflow"
+)
+
+agents.start()

agents/workflow_example_detailed.py

@@ -0,0 +1,253 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import random
+from typing import List, Dict, Union
+import json
+from pydantic import BaseModel
+
+# Add Pydantic models for data validation
+class Person(BaseModel):
+    name: str
+    age: int
+    job: str
+    city: str
+    salary: int
+
+class ProcessedPerson(Person):
+    salary_category: str
+    age_group: str
+    processing_status: str
+
+class DataList(BaseModel):
+    items: List[Dict]
+
+class ValidationResult(BaseModel):
+    validation_result: str
+    details: str = ""
+
+def random_data_of_individuals() -> List[Dict]:
+    """Generate random individual data"""
+    names = ["John", "Jane", "Mike", "Sarah", "David", "Emma"]
+    jobs = ["Engineer", "Doctor", "Teacher", "Artist", "Developer"]
+    cities = ["New York", "London", "Tokyo", "Paris", "Berlin"]
+    
+    data = []
+    for _ in range(random.randint(3, 7)):
+        person = {
+            "name": random.choice(names),
+            "age": random.randint(25, 65),
+            "job": random.choice(jobs),
+            "city": random.choice(cities),
+            "salary": random.randint(30000, 120000)
+        }
+        data.append(person)
+    return data
+
+def process_data_of_individuals(data: Union[List[Dict], Dict, str]) -> Dict:
+    """Process individual data by adding categories and analysis"""
+    try:
+        print("\n[DEBUG] process_data_of_individuals input:", data)
+        print("[DEBUG] Current workflow state:")
+        print("- total_records:", workflow.get_state("total_records"))
+        print("- current_index:", workflow.get_state("current_index"))
+        print("- remaining:", workflow.get_state("remaining"))
+
+        # Get the items list from the collect_task result
+        collect_result = None
+        for task in workflow.tasks.values():
+            if task.name == "collect_data" and task.result:
+                try:
+                    collect_data = json.loads(task.result.raw)
+                    collect_result = collect_data.get("items", [])
+                    print("[DEBUG] Found collect_data items:", len(collect_result))
+                except:
+                    print("[DEBUG] Failed to parse collect_data result")
+
+        # Handle string input by trying to parse it as JSON
+        if isinstance(data, str):
+            if ":" in data and not data.strip().startswith("{"):
+                # Convert string format to dictionary
+                pairs = [pair.strip() for pair in data.split(",")]
+                data_dict = {}
+                for pair in pairs:
+                    key, value = pair.split(":")
+                    key = key.strip().lower()
+                    value = value.strip()
+                    if key == "age" or key == "salary":
+                        value = int(value)
+                    data_dict[key] = value
+                data = data_dict
+            else:
+                data = json.loads(data)
+            print("[DEBUG] Parsed data:", data)
+
+        # Handle single record
+        if isinstance(data, dict):
+            person = data
+            # Initialize total records if not set
+            total_records = workflow.get_state("total_records")
+            if total_records is None and collect_result:
+                total_records = len(collect_result)
+                workflow.set_state("total_records", total_records)
+                print(f"[DEBUG] Initialized total_records to {total_records}")
+
+            current_index = workflow.get_state("current_index", 0)
+            total_records = total_records or 1
+            remaining = total_records - (current_index + 1)
+            workflow.set_state("remaining", remaining)
+            print(f"[DEBUG] Processing record {current_index + 1}/{total_records}")
+
+        elif isinstance(data, list):
+            if len(data) == 0:
+                raise ValueError("Empty data list")
+            person = data[0]
+            workflow.set_state("total_records", len(data))
+            workflow.set_state("current_index", 0)
+            workflow.set_state("remaining", len(data) - 1)
+            print(f"[DEBUG] First record from list of {len(data)} items")
+        else:
+            raise ValueError("Input must be a dictionary or list with at least one record")
+
+        processed_person = person.copy()
+        
+        # Add salary category
+        salary = person.get("salary", 0)
+        if salary < 50000:
+            processed_person["salary_category"] = "entry"
+        elif salary < 90000:
+            processed_person["salary_category"] = "mid"
+        else:
+            processed_person["salary_category"] = "senior"
+        
+        # Add age group
+        age = person.get("age", 0)
+        if age < 35:
+            processed_person["age_group"] = "young"
+        elif age < 50:
+            processed_person["age_group"] = "mid"
+        else:
+            processed_person["age_group"] = "senior"
+        
+        # Add processing status using workflow state
+        remaining = workflow.get_state("remaining", 0)
+        current_index = workflow.get_state("current_index", 0)
+        total_records = workflow.get_state("total_records", 1)
+        
+        # Update current index for next iteration
+        workflow.set_state("current_index", current_index + 1)
+        
+        print(f"[DEBUG] Status check - remaining: {remaining}, current_index: {current_index}, total_records: {total_records}")
+        
+        if remaining <= 0 and current_index >= total_records - 1:
+            print("[DEBUG] Setting status to 'all records processed'")
+            processed_person["processing_status"] = "all records processed"
+        else:
+            print(f"[DEBUG] More records to process. Remaining: {remaining}")
+            processed_person["processing_status"] = f"more records to process ({remaining} remaining)"
+            
+        print("[DEBUG] Final processed person:", processed_person)
+        return processed_person
+        
+    except Exception as e:
+        print(f"[DEBUG] Error processing data: {str(e)}")
+        return {"error": str(e), "processing_status": "error occurred"}
+
+# Create agents
+data_agent = Agent(
+    name="DataCollector",
+    role="Data collection specialist",
+    goal="Collect and validate data about individuals",
+    backstory="Expert in gathering and validating demographic data",
+    tools=[random_data_of_individuals],
+    self_reflect=False
+)
+
+process_agent = Agent(
+    name="DataProcessor",
+    role="Data processor",
+    goal="Process and categorize individual data",
+    backstory="Expert in data analysis and categorization",
+    tools=[process_data_of_individuals],
+    self_reflect=False
+)
+
+# Modify tasks to use Pydantic models
+collect_task = Task(
+    description="Collect random individual data using the random_data_of_individuals tool. Return as a JSON object with 'items' array.",
+    expected_output="List of individual records with basic information",
+    agent=data_agent,
+    name="collect_data",
+    tools=[random_data_of_individuals],
+    is_start=True,
+    next_tasks=["validate_data"],
+    output_json=DataList
+)
+
+validate_task = Task(
+    description="""Validate the collected data. Check if:
+    1. All required fields are present (name, age, job, city, salary)
+    2. Age is between 25 and 65
+    3. Salary is between 30000 and 120000
+    Return validation_result as 'valid' or 'invalid' with optional details.""",
+    expected_output="Validation result indicating if data is valid or invalid",
+    agent=data_agent,
+    name="validate_data",
+    task_type="decision",
+    condition={
+        "valid": ["process_data"],
+        "invalid": ["collect_data"]
+    },
+    output_json=ValidationResult
+)
+
+process_task = Task(
+    description="""Process one record at a time from the input data.
+    Current progress will be shown in Loop Status.
+    
+    For the current record:
+    1. Use process_data_of_individuals tool to add categories
+    2. Return the processed record with remaining count
+    
+    Current remaining: {remaining}
+    Current item: {current_item}
+    
+    Process this record and indicate if more records remain.""",
+    expected_output="Processed record with categories and status",
+    agent=process_agent,
+    name="process_data",
+    tools=[process_data_of_individuals],
+    task_type="loop",
+    condition={
+        "more records to process": ["process_data"],
+        "all records processed": []
+    },
+    context=[collect_task],
+    output_json=ProcessedPerson
+)
+
+# Create PraisonAIAgents instance with workflow process
+workflow = PraisonAIAgents(
+    agents=[data_agent, process_agent],
+    tasks=[collect_task, validate_task, process_task],
+    verbose=1,
+    process="workflow"
+)
+
+# Run the workflow
+result = workflow.start()
+
+# Print results
+print("\nWorkflow Results:")
+print("----------------")
+for task_id, task in workflow.tasks.items():
+    print(f"\nTask: {task.name}")
+    print(f"Status: {task.status}")
+    if task.result:
+        print("Output:")
+        try:
+            # Try to format as pretty JSON
+            import json
+            output = json.loads(task.result.raw)
+            print(json.dumps(output, indent=2))
+        except:
+            # If not JSON, print raw output
+            print(task.result.raw[:500] + "..." if len(task.result.raw) > 500 else task.result.raw) 

chainlit.md

@@ -1 +0,0 @@
-# Welcome to Praison AI! 🚀🤖

cookbooks/.gitignore

@@ -0,0 +1,2 @@
+*.txt
+*.csv

cookbooks/agents_playbook/agents-scrapewebitetool.yaml

@@ -1,42 +0,0 @@
-framework: crewai
-topic: create a movie about cat in mars based on https://blog.celtx.com/how-to-write-a-good-story/
-roles:
-  story_developer:
-    backstory: Experienced in crafting compelling narratives for a diverse audience.
-    goal: Develop an engaging storyline for a movie about cats in Mars
-    role: Story Developer
-    tasks:
-      storyline_creation:
-        description: Create a captivating storyline for a movie about cats in Mars
-          inspired by the blog post at https://blog.celtx.com/how-to-write-a-good-story/
-        expected_output: Detailed storyline with key plot points, character motivations,
-          and setting descriptions.
-    tools:
-    - 'ScrapeWebsiteTool'
-  screenwriter:
-    backstory: Proficient in translating complex narratives into engaging screenplay
-      format.
-    goal: Transform the storyline into a script
-    role: Screenwriter
-    tasks:
-      script_creation:
-        description: Transform the developed storyline into a script for the movie
-          about cats in Mars.
-        expected_output: Complete script with dialogues, scenes, and stage directions
-          ready for production.
-    tools:
-    - ''
-  director:
-    backstory: Experienced in visual storytelling and bringing scripts to life on
-      screen.
-    goal: Oversee the production of the movie
-    role: Director
-    tasks:
-      movie_production:
-        description: Take the script and lead the team to produce the movie 'Cats
-          in Mars' based on the provided content and script.
-        expected_output: Completed movie ready for distribution with compelling visuals
-          and engaging storytelling.
-    tools:
-    - ''
-dependencies: []

cookbooks/agents_playbook/create-movie-agents.yaml

@@ -1,42 +0,0 @@
-framework: crewai
-topic: create a movie about cat in mars based on https://www.grammarly.com/blog/how-to-write-a-story/
-roles:
-  story_developer:
-    backstory: Experienced in crafting compelling narratives for a diverse audience.
-    goal: Develop an engaging storyline for a movie about cats in Mars
-    role: Story Developer
-    tasks:
-      storyline_creation:
-        description: Create a captivating storyline for a movie about cats in Mars
-          inspired by the blog post at https://www.grammarly.com/blog/how-to-write-a-story/
-        expected_output: Detailed storyline with key plot points, character motivations,
-          and setting descriptions.
-    tools:
-    - 'ScrapeWebsiteTool'
-  screenwriter:
-    backstory: Proficient in translating complex narratives into engaging screenplay
-      format.
-    goal: Transform the storyline into a script
-    role: Screenwriter
-    tasks:
-      script_creation:
-        description: Transform the developed storyline into a script for the movie
-          about cats in Mars.
-        expected_output: Complete script with dialogues, scenes, and stage directions
-          ready for production.
-    tools:
-    - ''
-  director:
-    backstory: Experienced in visual storytelling and bringing scripts to life on
-      screen.
-    goal: Oversee the production of the movie
-    role: Director
-    tasks:
-      movie_production:
-        description: Take the script and lead the team to produce the movie 'Cats
-          in Mars' based on the provided content and script.
-        expected_output: Completed movie ready for distribution with compelling visuals
-          and engaging storytelling.
-    tools:
-    - ''
-dependencies: []

cookbooks/agents_playbook/examples/researchers.yml

@@ -1,41 +0,0 @@
-framework: "crewai"
-topic: "Artificial Intelligence"
-
-roles:
-  researcher:
-    role: "Senior Data Researcher"
-    goal: "Uncover cutting-edge developments in {topic}"
-    backstory: > 
-      You're a seasoned researcher with a knack for uncovering the latest
-      developments in {topic}. Known for your ability to find the most relevant
-      information and present it in a clear and concise manner.
-    tools:
-      - search_tool: 
-          engine: google
-    tasks:
-      research_task:
-        description: >
-          Conduct thorough research about {topic}, focusing on the latest 
-          trends and technologies.
-        expected_output: > 
-          A list of 10 bullet points summarizing key findings with links to 
-          relevant sources. 
-
-  reporting_analyst:
-    role: "Reporting Analyst"
-    goal: "Create detailed reports based on {topic} data analysis and research findings."
-    backstory: >
-      You're a meticulous analyst with a keen eye for detail. You're known for 
-      your ability to turn complex data into clear and concise reports, making 
-      it easy for others to understand and act on the information you provide.
-    tasks:
-      reporting_task:
-        description: >
-          Review the research findings and create a comprehensive report on 
-          {topic}. Include insights, potential opportunities, and recommendations.
-        expected_output: >
-          A well-structured report in Markdown format, ready for distribution.
-
-dependencies:
- - task: reporting_task
-   depends_on: research_task  

cookbooks/agents_playbook/examples/scriptwriters.yml

@@ -1,41 +0,0 @@
-framework: "autogen"
-topic: "Artificial Intelligence"
-
-roles:
-  narrative_designer:
-    role: "Narrative Designer"
-    goal: "Craft engaging storylines and narratives for {topic}"
-    backstory: >
-      You're a creative visionary skilled in weaving complex narratives and 
-      developing engaging story worlds within {topic}. Your expertise lies in 
-      conceptualizing the overarching story and ensuring it resonates with the audience.
-    tasks:
-      story_concept_development:
-        description: >
-          Develop a compelling story concept for {topic}, focusing on originality, 
-          thematic depth, and audience engagement. Outline the main narrative arcs,
-          characters, and settings.
-        expected_output: >
-          A comprehensive document outlining the story concept, including key narrative 
-          arcs, character bios, and settings descriptions.
-
-  scriptwriter:
-    role: "Scriptwriter"
-    goal: "Transform story concepts into detailed scripts for {topic}"
-    backstory: >
-      With a flair for dialogue and a deep understanding of pacing and structure,
-      you excel at bringing narratives to life through scriptwriting. Your work 
-      turns story concepts into blueprints for compelling {topic} experiences.
-    tasks:
-      scriptwriting_task:
-        description: >
-          Based on the narrative concept, write a detailed script for {topic}. 
-          Include dialogue, stage directions, and scene descriptions that bring 
-          the story to life.
-        expected_output: >
-          A detailed script ready for production, including dialogue, stage 
-          directions, and scene descriptions.
-
-dependencies:
- - task: scriptwriting_task
-   depends_on: story_concept_development  

cookbooks/agents_playbook/finance-research.yaml

@@ -1,43 +0,0 @@
-framework: crewai
-topic: research about nvidia growth
-roles:
-  data_collector:
-    backstory: An experienced researcher with the ability to efficiently collect and
-      organize vast amounts of data.
-    goal: Gather information on Nvidia's growth
-    role: Data Collector
-    tasks:
-      data_collection_task:
-        description: Collect data on Nvidia's growth from various sources such as
-          financial reports, news articles, and company announcements.
-        expected_output: A comprehensive document detailing data points on Nvidia's
-          growth over the years.
-    tools:
-    - 'YouSearchTool'
-  data_analyst:
-    backstory: Specializes in extracting insights from large datasets, proficient
-      in quantitative and qualitative analysis.
-    goal: Analyze the collected data to identify trends and patterns
-    role: Data Analyst
-    tasks:
-      data_analysis_task:
-        description: Analyze the collected data to identify key trends and patterns
-          in Nvidia's growth.
-        expected_output: An analytical report summarizing trends, patterns, and key
-          growth metrics of Nvidia.
-    tools:
-    - ''
-  report_preparer:
-    backstory: Experienced in creating detailed reports and presentations, turning
-      analytical data into actionable insights.
-    goal: Generate a final report on Nvidia's growth
-    role: Report Preparer
-    tasks:
-      report_preparation_task:
-        description: Create a detailed report based on the analysis, highlighting
-          Nvidia's growth trajectory.
-        expected_output: A polished, comprehensive report summarizing Nvidia's growth
-          with visual aids and key insights.
-    tools:
-    - ''
-dependencies: []

cookbooks/agents_playbook/internet-research-agents.yaml

@@ -1,26 +0,0 @@
-framework: crewai
-topic: research about the latest AI News and prepare a detailed report
-roles:
-  research_analyst:
-    backstory: Experienced in gathering and analyzing data related to AI news trends.
-    goal: Analyze AI News trends
-    role: Research Analyst
-    tasks:
-      gather_data:
-        description: Conduct in-depth research on the latest AI News trends from reputable
-          sources.
-        expected_output: Comprehensive report on current AI News trends.
-    tools:
-    - 'InternetSearchTool'
-  content_creator:
-    backstory: Skilled in writing informative and engaging content about AI News.
-    goal: Create detailed reports on AI News
-    role: Content Creator
-    tasks:
-      compile_report:
-        description: Compile all gathered data into a detailed report with analysis
-          and insights.
-        expected_output: Well-structured report on the latest AI News with key insights.
-    tools:
-    - ''
-dependencies: []

cookbooks/agents_playbook/medical-research-pubmed-agents.yaml

@@ -1,39 +0,0 @@
-framework: crewai
-topic: research about the causes of lung disease
-roles:
-  research_analyst:
-    backstory: Experienced in analyzing scientific data related to respiratory health.
-    goal: Analyze data on lung diseases
-    role: Research Analyst
-    tasks:
-      data_analysis:
-        description: Gather and analyze data on the causes and risk factors of lung
-          diseases.
-        expected_output: Report detailing key findings on lung disease causes.
-    tools:
-    - 'PubmedQueryRun'
-  medical_writer:
-    backstory: Skilled in translating complex medical information into accessible
-      content.
-    goal: Compile comprehensive content on lung disease causes
-    role: Medical Writer
-    tasks:
-      content_creation:
-        description: Create detailed content summarizing the research findings on
-          lung disease causes.
-        expected_output: Document outlining various causes and risk factors of lung
-          diseases.
-    tools:
-    - ''
-  editor:
-    backstory: Proficient in editing medical content for accuracy and clarity.
-    goal: Review and refine content on lung disease causes
-    role: Editor
-    tasks:
-      content_review:
-        description: Edit and refine the compiled content on lung disease causes for
-          accuracy and coherence.
-        expected_output: Finalized document on lung disease causes ready for dissemination.
-    tools:
-    - ''
-dependencies: []

cookbooks/praisonai-googlecolab.ipynb

@@ -1,311 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "cW47IVIvdyii",
-        "outputId": "dad73c8c-7506-440b-da37-8d8b21f7813f",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/173.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m173.7/173.7 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
-            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.9/40.9 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.3/67.3 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
-            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "\u001b[33mWARNING: embedchain 0.1.124 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.124 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.123 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.123 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.122 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.122 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.121 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.121 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.120 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.120 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.119 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.119 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.118 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.118 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.117 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
-            "\u001b[0m\u001b[33mWARNING: embedchain 0.1.117 does not provide the extra 'youtube'\u001b[0m\u001b[33m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m160.7/160.7 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.2/76.2 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.2/50.2 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.3/54.3 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m47.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m525.5/525.5 kB\u001b[0m \u001b[31m40.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m72.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.3/55.3 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m202.6/202.6 kB\u001b[0m \u001b[31m19.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m327.6/327.6 kB\u001b[0m \u001b[31m30.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.1/115.1 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.6/21.6 MB\u001b[0m \u001b[31m73.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.0/64.0 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.5/52.5 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m342.3/342.3 kB\u001b[0m \u001b[31m29.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.6/57.6 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m774.0/774.0 kB\u001b[0m \u001b[31m42.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.7/9.7 MB\u001b[0m \u001b[31m38.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m233.2/233.2 kB\u001b[0m \u001b[31m19.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m273.8/273.8 kB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m172.8/172.8 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.7/67.7 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.7/94.7 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m131.6/131.6 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m67.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m71.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.9/302.9 kB\u001b[0m \u001b[31m23.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.2/93.2 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.2/13.2 MB\u001b[0m \u001b[31m88.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.0/53.0 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.4/54.4 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.4/5.4 MB\u001b[0m \u001b[31m85.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m342.2/342.2 kB\u001b[0m \u001b[31m28.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.8/295.8 kB\u001b[0m \u001b[31m22.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.1/71.1 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m58.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m481.7/481.7 kB\u001b[0m \u001b[31m34.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.7/63.7 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m81.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m254.9/254.9 kB\u001b[0m \u001b[31m23.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m248.1/248.1 kB\u001b[0m \u001b[31m23.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m442.1/442.1 kB\u001b[0m \u001b[31m36.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.5/49.5 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m856.7/856.7 kB\u001b[0m \u001b[31m48.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.3/73.3 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.0/14.0 MB\u001b[0m \u001b[31m75.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m91.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m425.7/425.7 kB\u001b[0m \u001b[31m32.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m164.1/164.1 kB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m78.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.6/78.6 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.4/194.4 kB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m72.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m80.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m67.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Building wheel for praisonai (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for docx2txt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install -Uq \"praisonai[crewai]\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "myh-_QCuHfOs",
-        "outputId": "5384b8e0-e56c-4833-a829-2593ba6c6393"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[1m\u001b[95m [2024-10-31 21:43:52][DEBUG]: == Working Agent: Space Researcher\u001b[00m\n",
-            "\u001b[1m\u001b[95m [2024-10-31 21:43:52][INFO]: == Starting Task: Research and compile information about exoplanets discovered in the last decade.\u001b[00m\n",
-            "\n",
-            "\n",
-            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
-            "\u001b[32;1m\u001b[1;3mThought: I now can give a great answer  \n",
-            "Final Answer: my best complete final answer to the task.  \n",
-            "Over the last decade, the field of exoplanet research has experienced remarkable advancements, leading to the discovery of thousands of new planets beyond our solar system. Here is a summarized report on some of the most significant exoplanet discoveries in terms of their size, potential habitability, and distance from Earth:\n",
-            "\n",
-            "1. **Kepler-452b**: Discovered in 2015 by NASA's Kepler mission, Kepler-452b is often referred to as Earth's \"cousin.\" It is about 1.6 times the size of Earth and orbits within the habitable zone of a G-type star similar to our Sun, located approximately 1,400 light-years away from Earth. Its potential for hosting life lies in its Earth-like characteristics, though its actual habitability remains uncertain due to its distance and lack of detailed atmospheric data.\n",
-            "\n",
-            "2. **Proxima Centauri b**: Found in 2016, this exoplanet orbits the closest star to our solar system, Proxima Centauri, at a distance of just 4.24 light-years. Proxima Centauri b is roughly 1.27 times the size of Earth and resides within the star's habitable zone, where liquid water could potentially exist. Its proximity makes it a prime candidate for future studies on habitability and atmospheric composition.\n",
-            "\n",
-            "3. **TRAPPIST-1 System**: Discovered in 2017, the TRAPPIST-1 system contains seven Earth-sized exoplanets, three of which lie in the star's habitable zone. Located about 39 light-years away, these planets are of significant interest due to their potential for liquid water. The compact nature of the system and the small, cool star around which they orbit make them excellent targets for atmospheric studies using telescopes like the James Webb Space Telescope.\n",
-            "\n",
-            "4. **LHS 1140 b**: This exoplanet was discovered in 2017 and is located approximately 41 light-years away. It is about 1.4 times the size of Earth and orbits within the habitable zone of a red dwarf star. Its dense, rocky composition and the presence of a substantial atmosphere make it a compelling candidate for habitability studies.\n",
-            "\n",
-            "5. **K2-18b**: Announced in 2019, K2-18b is situated 124 light-years from Earth and is a super-Earth with a mass about 8 times that of our planet. It orbits within the habitable zone of its star, and water vapor has been detected in its atmosphere, making it one of the most intriguing exoplanets in terms of potential habitability.\n",
-            "\n",
-            "These discoveries highlight the diversity of exoplanets in terms of size, composition, and orbit, as well as the progress made in identifying worlds that could potentially host life. Advances in technology and observational techniques continue to enhance our understanding of these distant worlds, bringing us closer to answering the age-old question of whether we are alone in the universe.\u001b[0m\n",
-            "\n",
-            "\u001b[1m> Finished chain.\u001b[0m\n",
-            "\u001b[1m\u001b[92m [2024-10-31 21:44:05][DEBUG]: == [Space Researcher] Task output: my best complete final answer to the task.  \n",
-            "Over the last decade, the field of exoplanet research has experienced remarkable advancements, leading to the discovery of thousands of new planets beyond our solar system. Here is a summarized report on some of the most significant exoplanet discoveries in terms of their size, potential habitability, and distance from Earth:\n",
-            "\n",
-            "1. **Kepler-452b**: Discovered in 2015 by NASA's Kepler mission, Kepler-452b is often referred to as Earth's \"cousin.\" It is about 1.6 times the size of Earth and orbits within the habitable zone of a G-type star similar to our Sun, located approximately 1,400 light-years away from Earth. Its potential for hosting life lies in its Earth-like characteristics, though its actual habitability remains uncertain due to its distance and lack of detailed atmospheric data.\n",
-            "\n",
-            "2. **Proxima Centauri b**: Found in 2016, this exoplanet orbits the closest star to our solar system, Proxima Centauri, at a distance of just 4.24 light-years. Proxima Centauri b is roughly 1.27 times the size of Earth and resides within the star's habitable zone, where liquid water could potentially exist. Its proximity makes it a prime candidate for future studies on habitability and atmospheric composition.\n",
-            "\n",
-            "3. **TRAPPIST-1 System**: Discovered in 2017, the TRAPPIST-1 system contains seven Earth-sized exoplanets, three of which lie in the star's habitable zone. Located about 39 light-years away, these planets are of significant interest due to their potential for liquid water. The compact nature of the system and the small, cool star around which they orbit make them excellent targets for atmospheric studies using telescopes like the James Webb Space Telescope.\n",
-            "\n",
-            "4. **LHS 1140 b**: This exoplanet was discovered in 2017 and is located approximately 41 light-years away. It is about 1.4 times the size of Earth and orbits within the habitable zone of a red dwarf star. Its dense, rocky composition and the presence of a substantial atmosphere make it a compelling candidate for habitability studies.\n",
-            "\n",
-            "5. **K2-18b**: Announced in 2019, K2-18b is situated 124 light-years from Earth and is a super-Earth with a mass about 8 times that of our planet. It orbits within the habitable zone of its star, and water vapor has been detected in its atmosphere, making it one of the most intriguing exoplanets in terms of potential habitability.\n",
-            "\n",
-            "These discoveries highlight the diversity of exoplanets in terms of size, composition, and orbit, as well as the progress made in identifying worlds that could potentially host life. Advances in technology and observational techniques continue to enhance our understanding of these distant worlds, bringing us closer to answering the age-old question of whether we are alone in the universe.\n",
-            "\n",
-            "\u001b[00m\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "### Task Output ###\n",
-              "my best complete final answer to the task.  \n",
-              "Over the last decade, the field of exoplanet research has experienced remarkable advancements, leading to the \n",
-              "discovery of thousands of new planets beyond our solar system. Here is a summarized report on some of the most \n",
-              "significant exoplanet discoveries in terms of their size, potential habitability, and distance from Earth:\n",
-              "\n",
-              "\u001b[1;36m1\u001b[0m. **Kepler-452b**: Discovered in \u001b[1;36m2015\u001b[0m by NASA's Kepler mission, Kepler-452b is often referred to as Earth's \n",
-              "\u001b[32m\"cousin.\"\u001b[0m It is about \u001b[1;36m1.6\u001b[0m times the size of Earth and orbits within the habitable zone of a G-type star similar to \n",
-              "our Sun, located approximately \u001b[1;36m1\u001b[0m,\u001b[1;36m400\u001b[0m light-years away from Earth. Its potential for hosting life lies in its \n",
-              "Earth-like characteristics, though its actual habitability remains uncertain due to its distance and lack of \n",
-              "detailed atmospheric data.\n",
-              "\n",
-              "\u001b[1;36m2\u001b[0m. **Proxima Centauri b**: Found in \u001b[1;36m2016\u001b[0m, this exoplanet orbits the closest star to our solar system, Proxima \n",
-              "Centauri, at a distance of just \u001b[1;36m4.24\u001b[0m light-years. Proxima Centauri b is roughly \u001b[1;36m1.27\u001b[0m times the size of Earth and \n",
-              "resides within the star's habitable zone, where liquid water could potentially exist. Its proximity makes it a \n",
-              "prime candidate for future studies on habitability and atmospheric composition.\n",
-              "\n",
-              "\u001b[1;36m3\u001b[0m. **TRAPPIST-\u001b[1;36m1\u001b[0m System**: Discovered in \u001b[1;36m2017\u001b[0m, the TRAPPIST-\u001b[1;36m1\u001b[0m system contains seven Earth-sized exoplanets, three of\n",
-              "which lie in the star's habitable zone. Located about \u001b[1;36m39\u001b[0m light-years away, these planets are of significant \n",
-              "interest due to their potential for liquid water. The compact nature of the system and the small, cool star around \n",
-              "which they orbit make them excellent targets for atmospheric studies using telescopes like the James Webb Space \n",
-              "Telescope.\n",
-              "\n",
-              "\u001b[1;36m4\u001b[0m. **LHS \u001b[1;36m1140\u001b[0m b**: This exoplanet was discovered in \u001b[1;36m2017\u001b[0m and is located approximately \u001b[1;36m41\u001b[0m light-years away. It is \n",
-              "about \u001b[1;36m1.4\u001b[0m times the size of Earth and orbits within the habitable zone of a red dwarf star. Its dense, rocky \n",
-              "composition and the presence of a substantial atmosphere make it a compelling candidate for habitability studies.\n",
-              "\n",
-              "\u001b[1;36m5\u001b[0m. **K2-18b**: Announced in \u001b[1;36m2019\u001b[0m, K2-18b is situated \u001b[1;36m124\u001b[0m light-years from Earth and is a super-Earth with a mass \n",
-              "about \u001b[1;36m8\u001b[0m times that of our planet. It orbits within the habitable zone of its star, and water vapor has been \n",
-              "detected in its atmosphere, making it one of the most intriguing exoplanets in terms of potential habitability.\n",
-              "\n",
-              "These discoveries highlight the diversity of exoplanets in terms of size, composition, and orbit, as well as the \n",
-              "progress made in identifying worlds that could potentially host life. Advances in technology and observational \n",
-              "techniques continue to enhance our understanding of these distant worlds, bringing us closer to answering the \n",
-              "age-old question of whether we are alone in the universe.\n"
-            ],
-            "text/html": [
-              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">### Task Output ###\n",
-              "my best complete final answer to the task.  \n",
-              "Over the last decade, the field of exoplanet research has experienced remarkable advancements, leading to the \n",
-              "discovery of thousands of new planets beyond our solar system. Here is a summarized report on some of the most \n",
-              "significant exoplanet discoveries in terms of their size, potential habitability, and distance from Earth:\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>. **Kepler-452b**: Discovered in <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2015</span> by NASA's Kepler mission, Kepler-452b is often referred to as Earth's \n",
-              "<span style=\"color: #008000; text-decoration-color: #008000\">\"cousin.\"</span> It is about <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.6</span> times the size of Earth and orbits within the habitable zone of a G-type star similar to \n",
-              "our Sun, located approximately <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>,<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">400</span> light-years away from Earth. Its potential for hosting life lies in its \n",
-              "Earth-like characteristics, though its actual habitability remains uncertain due to its distance and lack of \n",
-              "detailed atmospheric data.\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>. **Proxima Centauri b**: Found in <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2016</span>, this exoplanet orbits the closest star to our solar system, Proxima \n",
-              "Centauri, at a distance of just <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4.24</span> light-years. Proxima Centauri b is roughly <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.27</span> times the size of Earth and \n",
-              "resides within the star's habitable zone, where liquid water could potentially exist. Its proximity makes it a \n",
-              "prime candidate for future studies on habitability and atmospheric composition.\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>. **TRAPPIST-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> System**: Discovered in <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2017</span>, the TRAPPIST-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> system contains seven Earth-sized exoplanets, three of\n",
-              "which lie in the star's habitable zone. Located about <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">39</span> light-years away, these planets are of significant \n",
-              "interest due to their potential for liquid water. The compact nature of the system and the small, cool star around \n",
-              "which they orbit make them excellent targets for atmospheric studies using telescopes like the James Webb Space \n",
-              "Telescope.\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>. **LHS <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1140</span> b**: This exoplanet was discovered in <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2017</span> and is located approximately <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">41</span> light-years away. It is \n",
-              "about <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.4</span> times the size of Earth and orbits within the habitable zone of a red dwarf star. Its dense, rocky \n",
-              "composition and the presence of a substantial atmosphere make it a compelling candidate for habitability studies.\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>. **K2-18b**: Announced in <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2019</span>, K2-18b is situated <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">124</span> light-years from Earth and is a super-Earth with a mass \n",
-              "about <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span> times that of our planet. It orbits within the habitable zone of its star, and water vapor has been \n",
-              "detected in its atmosphere, making it one of the most intriguing exoplanets in terms of potential habitability.\n",
-              "\n",
-              "These discoveries highlight the diversity of exoplanets in terms of size, composition, and orbit, as well as the \n",
-              "progress made in identifying worlds that could potentially host life. Advances in technology and observational \n",
-              "techniques continue to enhance our understanding of these distant worlds, bringing us closer to answering the \n",
-              "age-old question of whether we are alone in the universe.\n",
-              "</pre>\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "None\n"
-          ]
-        }
-      ],
-      "source": [
-        "import os\n",
-        "import yaml\n",
-        "from praisonai import PraisonAI\n",
-        "from google.colab import userdata\n",
-        "\n",
-        "# Example agent_yaml content\n",
-        "agent_yaml = \"\"\"\n",
-        "framework: \"crewai\"\n",
-        "topic: \"Space Exploration\"\n",
-        "\n",
-        "roles:\n",
-        "  astronomer:\n",
-        "    role: \"Space Researcher\"\n",
-        "    goal: \"Discover new insights about {topic}\"\n",
-        "    backstory: \"You are a curious and dedicated astronomer with a passion for unraveling the mysteries of the cosmos.\"\n",
-        "    tasks:\n",
-        "      investigate_exoplanets:\n",
-        "        description: \"Research and compile information about exoplanets discovered in the last decade.\"\n",
-        "        expected_output: \"A summarized report on exoplanet discoveries, including their size, potential habitability, and distance from Earth.\"\n",
-        "\"\"\"\n",
-        "\n",
-        "# Create a PraisonAI instance with the agent_yaml content\n",
-        "praisonai = PraisonAI(agent_yaml=agent_yaml)\n",
-        "\n",
-        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
-        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "\n",
-        "# Run PraisonAI\n",
-        "result = praisonai.run()\n",
-        "\n",
-        "# Print the result\n",
-        "print(result)"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "T4",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

cookbooks/praisonai-tools-googlecolab.ipynb

@@ -1,358 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": [
-        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/praisonai-tools-googlecolab.ipynb)"
-      ],
-      "metadata": {
-        "id": "MHwYRTSdlNUS"
-      }
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "id": "cW47IVIvdyii",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "ad31a979-acb4-4200-9ce8-439ab4193594"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/173.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m173.7/173.7 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
-            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.9/40.9 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.3/67.3 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
-            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
-            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "\u001b[33mWARNING: embedchain 0.1.124 does not provide the extra 'github'\u001b[0m\u001b[33m\n",
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-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m71.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m94.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Building wheel for praisonai (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for docx2txt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install -Uq \"praisonai[crewai]\" duckduckgo_search"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "id": "Qwds0ad96Pco"
-      },
-      "outputs": [],
-      "source": [
-        "# tools.py\n",
-        "from duckduckgo_search import DDGS\n",
-        "from praisonai_tools import BaseTool\n",
-        "\n",
-        "class InternetSearchTool(BaseTool):\n",
-        "    name: str = \"InternetSearchTool\"\n",
-        "    description: str = \"Search Internet for relevant information based on a query or latest news\"\n",
-        "\n",
-        "    def _run(self, query: str):\n",
-        "        ddgs = DDGS()\n",
-        "        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)\n",
-        "        return results"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "myh-_QCuHfOs",
-        "outputId": "8024c5db-cdf3-4099-cf15-86c33c35f795"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[1m\u001b[95m [2024-10-31 17:13:31][DEBUG]: == Working Agent: Space Researcher\u001b[00m\n",
-            "\u001b[1m\u001b[95m [2024-10-31 17:13:31][INFO]: == Starting Task: Research and compile information about exoplanets discovered in the last decade.\u001b[00m\n",
-            "\n",
-            "\n",
-            "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n",
-            "\u001b[32;1m\u001b[1;3mTo compile a comprehensive report on exoplanets discovered in the last decade, I should start by searching for relevant information on recent exoplanet discoveries, focusing on their size, potential habitability, and distance from Earth. \n",
-            "\n",
-            "Action: InternetSearchTool\n",
-            "Action Input: {\"query\": \"exoplanets discovered in the last decade size habitability distance from Earth\"}\u001b[0m"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:214: UserWarning: A custom validator is returning a value other than `self`.\n",
-            "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n",
-            "See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.\n",
-            "  warnings.warn(\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[95m \n",
-            "\n",
-            "[{'title': 'Earth-Size, Habitable Zone Planet Found Hidden in Early NASA Kepler ...', 'href': 'https://www.nasa.gov/news-release/earth-size-habitable-zone-planet-found-hidden-in-early-nasa-kepler-data/', 'body': \"A team of transatlantic scientists, using reanalyzed data from NASA's Kepler space telescope, has discovered an Earth-size exoplanet orbiting in its star's habitable zone, the area around a star where a rocky planet could support liquid water. Scientists discovered this planet, called Kepler-1649c, when looking through old observations from ...\"}, {'title': 'Largest Batch of Earth-size Habitable Zone Planets Found ...', 'href': 'https://science.nasa.gov/exoplanets/trappist1/', 'body': \"TRAPPIST-1: Largest Batch of Earth-sized Exoplanets The most studied planetary system, aside from our own solar system, lies about 40 light-years away. We've looked at the seven rocky exoplanets orbiting the TRAPPIST-1 star with ground and space telescopes like Spitzer, Kepler, Hubble, and, now, the James Webb Space Telescope. In March 2023, the first science […]\"}, {'title': 'NASA Telescope Reveals Largest Batch of Earth-Size ...', 'href': 'https://www.nasa.gov/news-release/nasa-telescope-reveals-largest-batch-of-earth-size-habitable-zone-planets-around-single-star/', 'body': '\"The seven wonders of TRAPPIST-1 are the first Earth-size planets that have been found orbiting this kind of star,\" said Michael Gillon, lead author of the paper and the principal investigator of the TRAPPIST exoplanet survey at the University of Liege, Belgium. \"It is also the best target yet for studying the atmospheres of potentially ...'}, {'title': 'NASA finds potentially habitable Earth-sized exoplanet ...', 'href': 'https://www.usatoday.com/story/news/nation/2024/05/28/nasa-exoplanet-discovery/73882055007/', 'body': '0:00. 0:59. NASA announced the discovery of a planet 40 light years from Earth that orbits every 12.8 days and is possibly even habitable. Gliese 12 b is a \"super Earth exoplanet\" that is nearly ...'}, {'title': \"NASA space telescope finds Earth-size exoplanet that's\", 'href': 'https://www.space.com/gliese-12-b-tess-exoplanet-hunt-for-life', 'body': \"The exoplanet — which the team found with NASA's Transiting Exoplanet Survey Satellite (TESS) — is estimated to have a width around 1.1 times that of Earth, making it similar to our planet as ...\"}]\n",
-            "\u001b[00m\n",
-            "\u001b[32;1m\u001b[1;3mThought: I have gathered multiple sources discussing exoplanet discoveries, including details about their size, potential habitability, and distance from Earth. I will now compile this information into a comprehensive report.\n",
-            "\n",
-            "Final Answer:\n",
-            "\n",
-            "### Exoplanet Discoveries in the Last Decade\n",
-            "\n",
-            "1. **Kepler-1649c**\n",
-            "   - **Size:** Earth-sized\n",
-            "   - **Habitability:** This exoplanet orbits in its star's habitable zone, where a rocky planet could support liquid water.\n",
-            "   - **Distance from Earth:** Discovered using reanalyzed data from NASA's Kepler space telescope, the exact distance wasn't specified in the initial search result.\n",
-            "\n",
-            "2. **TRAPPIST-1 System**\n",
-            "   - **Size:** The system consists of seven Earth-sized exoplanets.\n",
-            "   - **Habitability:** The TRAPPIST-1 system is one of the most studied, and it provides the best targets for studying the atmospheres of potentially habitable planets.\n",
-            "   - **Distance from Earth:** About 40 light-years away.\n",
-            "\n",
-            "3. **Gliese 12 b**\n",
-            "   - **Size:** Classified as a \"super Earth\" exoplanet, with a width approximately 1.1 times that of Earth.\n",
-            "   - **Habitability:** Potentially habitable and orbits every 12.8 days, suggesting it could support liquid water.\n",
-            "   - **Distance from Earth:** 40 light-years away.\n",
-            "\n",
-            "These discoveries highlight significant strides in identifying exoplanets that resemble Earth in size and orbit within habitable zones. The proximity of these planets, particularly the TRAPPIST-1 system and Gliese 12 b, offers promising opportunities for future exploration and study of potentially habitable worlds beyond our solar system.\u001b[0m\n",
-            "\n",
-            "\u001b[1m> Finished chain.\u001b[0m\n",
-            "\u001b[1m\u001b[92m [2024-10-31 17:13:47][DEBUG]: == [Space Researcher] Task output: ### Exoplanet Discoveries in the Last Decade\n",
-            "\n",
-            "1. **Kepler-1649c**\n",
-            "   - **Size:** Earth-sized\n",
-            "   - **Habitability:** This exoplanet orbits in its star's habitable zone, where a rocky planet could support liquid water.\n",
-            "   - **Distance from Earth:** Discovered using reanalyzed data from NASA's Kepler space telescope, the exact distance wasn't specified in the initial search result.\n",
-            "\n",
-            "2. **TRAPPIST-1 System**\n",
-            "   - **Size:** The system consists of seven Earth-sized exoplanets.\n",
-            "   - **Habitability:** The TRAPPIST-1 system is one of the most studied, and it provides the best targets for studying the atmospheres of potentially habitable planets.\n",
-            "   - **Distance from Earth:** About 40 light-years away.\n",
-            "\n",
-            "3. **Gliese 12 b**\n",
-            "   - **Size:** Classified as a \"super Earth\" exoplanet, with a width approximately 1.1 times that of Earth.\n",
-            "   - **Habitability:** Potentially habitable and orbits every 12.8 days, suggesting it could support liquid water.\n",
-            "   - **Distance from Earth:** 40 light-years away.\n",
-            "\n",
-            "These discoveries highlight significant strides in identifying exoplanets that resemble Earth in size and orbit within habitable zones. The proximity of these planets, particularly the TRAPPIST-1 system and Gliese 12 b, offers promising opportunities for future exploration and study of potentially habitable worlds beyond our solar system.\n",
-            "\n",
-            "\u001b[00m\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "### Task Output ###\n",
-              "### Exoplanet Discoveries in the Last Decade\n",
-              "\n",
-              "\u001b[1;36m1\u001b[0m. **Kepler-1649c**\n",
-              "   - **Size:** Earth-sized\n",
-              "   - **Habitability:** This exoplanet orbits in its star's habitable zone, where a rocky planet could support \n",
-              "liquid water.\n",
-              "   - **Distance from Earth:** Discovered using reanalyzed data from NASA's Kepler space telescope, the exact \n",
-              "distance wasn't specified in the initial search result.\n",
-              "\n",
-              "\u001b[1;36m2\u001b[0m. **TRAPPIST-\u001b[1;36m1\u001b[0m System**\n",
-              "   - **Size:** The system consists of seven Earth-sized exoplanets.\n",
-              "   - **Habitability:** The TRAPPIST-\u001b[1;36m1\u001b[0m system is one of the most studied, and it provides the best targets for \n",
-              "studying the atmospheres of potentially habitable planets.\n",
-              "   - **Distance from Earth:** About \u001b[1;36m40\u001b[0m light-years away.\n",
-              "\n",
-              "\u001b[1;36m3\u001b[0m. **Gliese \u001b[1;36m12\u001b[0m b**\n",
-              "   - **Size:** Classified as a \u001b[32m\"super Earth\"\u001b[0m exoplanet, with a width approximately \u001b[1;36m1.1\u001b[0m times that of Earth.\n",
-              "   - **Habitability:** Potentially habitable and orbits every \u001b[1;36m12.8\u001b[0m days, suggesting it could support liquid water.\n",
-              "   - **Distance from Earth:** \u001b[1;36m40\u001b[0m light-years away.\n",
-              "\n",
-              "These discoveries highlight significant strides in identifying exoplanets that resemble Earth in size and orbit \n",
-              "within habitable zones. The proximity of these planets, particularly the TRAPPIST-\u001b[1;36m1\u001b[0m system and Gliese \u001b[1;36m12\u001b[0m b, offers \n",
-              "promising opportunities for future exploration and study of potentially habitable worlds beyond our solar system.\n"
-            ],
-            "text/html": [
-              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">### Task Output ###\n",
-              "### Exoplanet Discoveries in the Last Decade\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>. **Kepler-1649c**\n",
-              "   - **Size:** Earth-sized\n",
-              "   - **Habitability:** This exoplanet orbits in its star's habitable zone, where a rocky planet could support \n",
-              "liquid water.\n",
-              "   - **Distance from Earth:** Discovered using reanalyzed data from NASA's Kepler space telescope, the exact \n",
-              "distance wasn't specified in the initial search result.\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>. **TRAPPIST-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> System**\n",
-              "   - **Size:** The system consists of seven Earth-sized exoplanets.\n",
-              "   - **Habitability:** The TRAPPIST-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> system is one of the most studied, and it provides the best targets for \n",
-              "studying the atmospheres of potentially habitable planets.\n",
-              "   - **Distance from Earth:** About <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span> light-years away.\n",
-              "\n",
-              "<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>. **Gliese <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span> b**\n",
-              "   - **Size:** Classified as a <span style=\"color: #008000; text-decoration-color: #008000\">\"super Earth\"</span> exoplanet, with a width approximately <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.1</span> times that of Earth.\n",
-              "   - **Habitability:** Potentially habitable and orbits every <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12.8</span> days, suggesting it could support liquid water.\n",
-              "   - **Distance from Earth:** <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span> light-years away.\n",
-              "\n",
-              "These discoveries highlight significant strides in identifying exoplanets that resemble Earth in size and orbit \n",
-              "within habitable zones. The proximity of these planets, particularly the TRAPPIST-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> system and Gliese <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span> b, offers \n",
-              "promising opportunities for future exploration and study of potentially habitable worlds beyond our solar system.\n",
-              "</pre>\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "None\n"
-          ]
-        }
-      ],
-      "source": [
-        "import os\n",
-        "import yaml\n",
-        "from praisonai import PraisonAI\n",
-        "from google.colab import userdata\n",
-        "\n",
-        "# Example agent_yaml content\n",
-        "agent_yaml = \"\"\"\n",
-        "framework: \"crewai\"\n",
-        "topic: \"Space Exploration\"\n",
-        "\n",
-        "roles:\n",
-        "  astronomer:\n",
-        "    role: \"Space Researcher\"\n",
-        "    goal: \"Discover new insights about {topic}\"\n",
-        "    backstory: \"You are a curious and dedicated astronomer with a passion for unraveling the mysteries of the cosmos.\"\n",
-        "    tasks:\n",
-        "      investigate_exoplanets:\n",
-        "        description: \"Research and compile information about exoplanets discovered in the last decade.\"\n",
-        "        expected_output: \"A summarized report on exoplanet discoveries, including their size, potential habitability, and distance from Earth.\"\n",
-        "    tools:\n",
-        "      - \"InternetSearchTool\"\n",
-        "\"\"\"\n",
-        "\n",
-        "# Create a PraisonAI instance with the agent_yaml content\n",
-        "praisonai = PraisonAI(agent_yaml=agent_yaml, tools=[InternetSearchTool])\n",
-        "\n",
-        "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
-        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "\n",
-        "# Run PraisonAI\n",
-        "result = praisonai.run()\n",
-        "\n",
-        "# Print the result\n",
-        "print(result)"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "T4",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

cookbooks/python/10_wikipedia_praison_ai_integration.py

@@ -1,67 +0,0 @@
-# tools.py
-from langchain_community.utilities import WikipediaAPIWrapper
-from praisonai_tools import BaseTool
-
-class WikipediaSearchTool(BaseTool):
-    name: str = "WikipediaSearchTool"
-    description: str = "Search Wikipedia for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = WikipediaAPIWrapper(top_k_results=4, doc_content_chars_max=100)
-        results = api_wrapper.load(query=query)
-        return results
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "research about Nvidia growth"
-roles:
-  data_collector:
-    role: "Data Collector"
-    backstory: "An experienced researcher with the ability to efficiently collect and organize vast amounts of data."
-    goal: "Gather information on Nvidia's growth by providing the Ticket Symbol to YahooFinanceNewsTool"
-    tasks:
-      data_collection_task:
-        description: "Collect data on Nvidia's growth from various sources such as financial reports, news articles, and company announcements."
-        expected_output: "A comprehensive document detailing data points on Nvidia's growth over the years."
-    tools:
-      - "WikipediaSearchTool"
-  data_analyst:
-    role: "Data Analyst"
-    backstory: "Specializes in extracting insights from large datasets, proficient in quantitative and qualitative analysis."
-    goal: "Analyze the collected data to identify trends and patterns"
-    tasks:
-      data_analysis_task:
-        description: "Analyze the collected data to identify key trends and patterns in Nvidia's growth."
-        expected_output: "An analytical report summarizing trends, patterns, and key growth metrics of Nvidia."
-    tools: []
-  report_preparer:
-    role: "Report Preparer"
-    backstory: "Experienced in creating detailed reports and presentations, turning analytical data into actionable insights."
-    goal: "Generate a final report on Nvidia's growth"
-    tasks:
-      report_preparation_task:
-        description: "Create a detailed report based on the analysis, highlighting Nvidia's growth trajectory."
-        expected_output: "A polished, comprehensive report summarizing Nvidia's growth with visual aids and key insights."
-    tools: []
-dependencies: []
-
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/11_crawl4ai_and_praison_ai_integration.py

@@ -1,97 +0,0 @@
-# tools.py
-import os
-from crawl4ai import WebCrawler
-from crawl4ai.extraction_strategy import LLMExtractionStrategy
-from pydantic import BaseModel, Field
-from praisonai_tools import BaseTool
-
-class ModelFee(BaseModel):
-    llm_model_name: str = Field(..., description="Name of the model.")
-    input_fee: str = Field(..., description="Fee for input token for the model.")
-    output_fee: str = Field(..., description="Fee for output token for the model.")
-
-class ModelFeeTool(BaseTool):
-    name: str = "ModelFeeTool"
-    description: str = "Extracts model fees for input and output tokens from the given pricing page."
-
-    def _run(self, url: str):
-        crawler = WebCrawler()
-        crawler.warmup()
-
-        result = crawler.run(
-            url=url,
-            word_count_threshold=1,
-            extraction_strategy= LLMExtractionStrategy(
-                provider="openai/gpt-4o",
-                api_token=os.getenv('OPENAI_API_KEY'), 
-                schema=ModelFee.schema(),
-                extraction_type="schema",
-                instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. 
-                Do not miss any models in the entire content. One extracted model JSON format should look like this: 
-                {"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}."""
-            ),            
-            bypass_cache=True,
-        )
-        return result.extracted_content
-    
-
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "extract model pricing from websites"
-roles:
-  web_scraper:
-    role: "Web Scraper"
-    backstory: |
-      An expert in web scraping with a deep understanding of extracting structured
-      data from online sources.
-
-      https://openai.com/api/pricing/
-      https://www.anthropic.com/pricing
-      https://cohere.com/pricing
-    goal: "Gather model pricing data from various websites"
-    tasks:
-      scrape_model_pricing:
-        description: "Scrape model pricing information from the provided list of websites."
-        expected_output: "Raw HTML or JSON containing model pricing data."
-    tools:
-      - "ModelFeeTool"
-  data_cleaner:
-    role: "Data Cleaner"
-    backstory: "Specialist in data cleaning, ensuring that all collected data is accurate and properly formatted."
-    goal: "Clean and organize the scraped pricing data"
-    tasks:
-      clean_pricing_data:
-        description: "Process the raw scraped data to remove any duplicates and inconsistencies, and convert it into a structured format."
-        expected_output: "Cleaned and organized JSON or CSV file with model pricing data."
-    tools: []
-  data_analyzer:
-    role: "Data Analyzer"
-    backstory: "Data analysis expert focused on deriving actionable insights from structured data."
-    goal: "Analyze the cleaned pricing data to extract insights"
-    tasks:
-      analyze_pricing_data:
-        description: "Analyze the cleaned data to extract trends, patterns, and insights on model pricing."
-        expected_output: "Detailed report summarizing model pricing trends and insights."
-    tools: []
-dependencies: []
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/1_business_product_launch_agents.py

@@ -1,66 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-
-# Example agents_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agents_yaml = """framework: "crewai"
-topic: "Product Development"
-roles:
-  market_research_analyst:
-    role: "Market Research Analyst"
-    backstory: "Expert at understanding market demand, target audience, and competition for products like the product. Skilled in developing marketing strategies to reach a wide audience."
-    goal: "Analyze the market demand for the product and suggest marketing strategies using InternetSearchTool."
-    tasks:
-      market_analysis_task:
-        description: "Analyze the market demand for the product. Current month is Jan 2024. Write a report on the ideal customer profile and marketing strategies to reach the widest possible audience. Include at least 10 bullet points addressing key marketing areas. Search the internet using InternetSearchTool."
-        expected_output: "Report on the ideal customer profile and marketing strategies, with at least 10 bullet points addressing key marketing areas."
-    tools:
-      - InternetSearchTool
-  technology_expert:
-    role: "Technology Expert"
-    backstory: "Visionary in current and emerging technological trends, especially in products like the product. Identifies which technologies are best suited for different business models."
-    goal: "Assess technological feasibilities and requirements for producing high-quality the product"
-    tasks:
-      technology_assessment_task:
-        description: "Assess the technological aspects of manufacturing high-quality the product. Write a report detailing necessary technologies and manufacturing approaches. Include at least 10 bullet points on key technological areas."
-        expected_output: "Report detailing necessary technologies and manufacturing approaches, including at least 10 bullet points on key technological areas."
-  business_consultant:
-    role: "Business Development Consultant"
-    backstory: "Seasoned in shaping business strategies for products like the product. Understands scalability and potential revenue streams to ensure long-term sustainability."
-    goal: "Evaluate the business model for the product, focusing on scalability and revenue streams"
-    tasks:
-      business_model_evaluation_task:
-        description: "Summarize the market and technological reports and evaluate the business model for the product. Write a report on the scalability and revenue streams for the product. Include at least 10 bullet points on key business areas. Give Business Plan, Goals, and Timeline for the product launch. Current month is Jan 2024."
-        expected_output: "Comprehensive business model evaluation report including scalability, revenue streams, at least 10 key business areas, Business Plan, Goals, and Timeline for the product launch."
-      """
-
-
-# Create a PraisonAI instance with the agent_yaml content and tools
-praisonai = PraisonAI(
-    agent_yaml=agents_yaml,
-    tools=[InternetSearchTool]  # Just pass the class directly in a list
-)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/2_create_story_book_agent.py

@@ -1,80 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-# Example agent_yaml content
-
-import os
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "Children's Storybook Creation"
-roles:
-  story_outliner:
-    role: "Story Outliner"
-    backstory: "An imaginative creator who lays the foundation of captivating stories for children."
-    goal: "Develop an outline for a children's storybook about Animals, including chapter titles and characters for 5 chapters."
-    tasks:
-      task_outline:
-        description: "Create an outline for the children's storybook about Animals, detailing chapter titles and character descriptions for 5 chapters."
-        expected_output: "A structured outline document containing 5 chapter titles, with detailed character descriptions and the main plot points for each chapter."
-    tools:
-      - InternetSearchTool
-  story_writer:
-    role: "Story Writer"
-    backstory: "A talented storyteller who brings to life the world and characters outlined, crafting engaging and imaginative tales for children."
-    goal: "Write the full content of the story for all 5 chapters, each chapter 100 words, weaving together the narratives and characters outlined."
-    tasks:
-      task_write:
-        description: "Using the outline provided, write the full story content for all chapters, ensuring a cohesive and engaging narrative for children. Each chapter should contain approximately 100 words. Include the title of the story at the top."
-        expected_output: "A complete manuscript of the children's storybook about Animals with 5 chapters, each approximately 100 words, following the provided outline and integrating the characters and plot points into a cohesive narrative."
-  image_generator:
-    role: "Image Generator"
-    backstory: "A creative AI specialized in visual storytelling, bringing each chapter to life through imaginative imagery."
-    goal: "Generate one image per chapter based on the content provided by the story writer. Start with chapter number, chapter content, character details, detailed location information, and detailed items in the location where the activity happens. Generate a total of 5 images one by one. Final output should contain all 5 images in JSON format."
-    tasks:
-      task_image_generate:
-        description: "Generate 5 images that capture the essence of the children's storybook about Animals, aligning with the themes, characters, and narratives outlined in the chapters. Do it one by one."
-        expected_output: "A set of digital images that visually represent each chapter of the children's storybook, incorporating elements from the characters and plot as described in the chapters. The images should be suitable for inclusion in the storybook as illustrations."
-  content_formatter:
-    role: "Content Formatter"
-    backstory: "A meticulous formatter who enhances the readability and presentation of the storybook."
-    goal: "Format the written story content in markdown, including images at the beginning of each chapter."
-    tasks:
-      task_format_content:
-        description: "Format the story content in markdown, including an image at the beginning of each chapter."
-        expected_output: "The entire storybook content formatted in markdown, with each chapter title followed by the corresponding image and the chapter content."
-        output_file: "story.md"
-  markdown_to_pdf_creator:
-    role: "PDF Converter"
-    backstory: "An efficient converter that transforms Markdown files into professionally formatted PDF documents."
-    goal: "Convert the Markdown file to a PDF document. 'story.md' is the markdown file name."
-    tasks:
-      task_markdown_to_pdf:
-        description: "Convert a Markdown file to a PDF document, ensuring the preservation of formatting, structure, and embedded images using the mdpdf library."
-        expected_output: "A PDF file generated from the Markdown input, accurately reflecting the content with proper formatting. The PDF should be ready for sharing or printing."
-
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/3_crewai_open_interpreter.py

@@ -1,51 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "Command Line Operations"
-
-roles:
-  cli_agent:
-    role: "Software Engineer"
-    backstory: "Expert in command line operations, creating and executing code."
-    goal: "Always use Executor Tool. Ability to perform CLI operations, write programs, and execute using Executor Tool."
-    tools:
-      - "CLITool.execute_cli_command"
-    tasks:
-      cli_task:
-        description: "Identify the operating system."
-        expected_output: "Successfully identifies the OS."
-
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/4_rag_using_tools.py

@@ -1,75 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-              framework: "crewai"
-              topic: "News Analysis"
-
-              roles:
-                news_search_agent:
-                  role: "News Searcher"
-                  backstory: "Expert in analyzing and generating key points from news content for quick updates."
-                  goal: "Generate key points for each news article from the latest news."
-                  tools:
-                    - "SearchNewsDB.news"
-                  allow_delegation: true
-                  tasks:
-                    news_search_task:
-                      description: "Search for 'AI 2024' and create key points for each news."
-                      expected_output: "Key points for each news article related to 'AI 2024'."
-                writer_agent:
-                  role: "Writer"
-                  backstory: "Expert in crafting engaging narratives from complex information."
-                  goal: "Identify all the topics received. Use the Get News Tool to verify each topic to search. Use the Search tool for detailed exploration of each topic. Summarize the retrieved information in depth for every topic."
-                  tools:
-                    - "GetNews.news"
-                    - "DuckDuckGoSearchRun"
-                  allow_delegation: true
-                  tasks:
-                    writer_task:
-                      description: |
-                        Go step by step.
-                        Step 1: Identify all the topics received.
-                        Step 2: Use the Get News Tool to verify each topic by going through one by one.
-                        Step 3: Use the Search tool to search for information on each topic one by one.
-                        Step 4: Go through every topic and write an in-depth summary of the information retrieved.
-                        Don't skip any topic.
-                      expected_output: "An in-depth summary of the information retrieved for each topic."
-
-              dependencies:
-                - task: "writer_task"
-                  depends_on:
-                    - "news_search_task"
-
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/5_job_search.py

@@ -1,94 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "Job Search Assistance"
-
-roles:
-  job_searcher_agent:
-    role: "Job Searcher"
-    backstory: "You are actively searching for job opportunities in your field, ready to utilize and expand your skill set in a new role."
-    goal: "Search for jobs in the field of interest, focusing on enhancing relevant skills."
-    tools:
-      - "JobSearchTools.search_jobs"
-    allow_delegation: true
-    tasks:
-      job_search_task:
-        description: |
-          Search for current job openings for the Senior Data Scientist role in New York using the Job Search tool. Find 5 vacant positions in total. Emphasize the key skills required. The tool accepts input in JSON format with the following schema: 'role': '<role>', 'location': '<location>', 'num_results': <number>. Ensure to format the input accordingly.
-        expected_output: "A list of 5 Senior Data Scientist job openings in New York, including key skills required for each position."
-
-  skills_development_agent:
-    role: "Skills Development Advisor"
-    backstory: "As a skills development advisor, you assist job searchers in identifying crucial skills for their target roles and recommend ways to develop these skills."
-    goal: "Identify key skills required for jobs of interest and advise on improving them."
-    allow_delegation: true
-    tasks:
-      skills_highlighting_task:
-        description: |
-          Based on the identified job openings, list the key skills required for each position separately. Provide recommendations on how candidates can acquire or improve these skills through courses, self-study, or practical experience.
-        expected_output: "A detailed list of key skills for each position with recommendations on how to develop them."
-
-  interview_preparation_coach:
-    role: "Interview Preparation Coach"
-    backstory: "Expert in coaching job searchers on successful interview techniques, including mock interviews and feedback."
-    goal: "Enhance interview skills, focusing on common questions, presentation, and communication."
-    allow_delegation: true
-    tasks:
-      interview_preparation_task:
-        description: |
-          Prepare job searchers for interviews by conducting mock interviews and offering feedback on their responses, presentation, and communication skills, for each role separately.
-        expected_output: "Feedback and guidance on interview skills for each role, including common questions and tips on presentation and communication."
-
-  career_advisor:
-    role: "Career Advisor"
-    backstory: "Experienced in guiding candidates through their job search journey, offering personalized advice on career development and application processes."
-    goal: "Assist in resume building, LinkedIn profile optimization, and networking strategies."
-    allow_delegation: true
-    tasks:
-      career_advisory_task:
-        description: |
-          Offer guidance on resume building, optimizing LinkedIn profiles, and effective networking strategies to enhance job application success, for each role separately.
-        expected_output: "Personalized advice on resume enhancement, LinkedIn optimization, and networking strategies for each position."
-
-dependencies:
-  - task: "skills_highlighting_task"
-    depends_on:
-      - "job_search_task"
-  - task: "interview_preparation_task"
-    depends_on:
-      - "job_search_task"
-  - task: "career_advisory_task"
-    depends_on:
-      - "job_search_task"
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/6_rag_2-0.py

@@ -1,103 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "AI in healthcare"
-
-roles:
-  manager:
-    role: "Project Manager"
-    backstory: |
-      With a strategic mindset and a knack for leadership, you excel at guiding teams towards
-      their goals, ensuring projects not only meet but exceed expectations.
-    goal: "Coordinate the project to ensure a seamless integration of research findings into compelling narratives"
-    verbose: true
-    allow_delegation: true
-    max_iter: 10
-    max_rpm: 20
-    tasks:
-      manager_task:
-        description: |
-          Oversee the integration of research findings and narrative development to produce a final comprehensive
-          report on AI in healthcare. Ensure the research is accurately represented and the narrative is engaging and informative.
-        expected_output: "A final comprehensive report that combines the research findings and narrative on AI in healthcare."
-
-  researcher:
-    role: "Senior Researcher"
-    backstory: |
-      Driven by curiosity, you're at the forefront of innovation, eager to explore and share
-      knowledge that could change the world.
-    goal: "Uncover groundbreaking technologies around AI in healthcare"
-    verbose: true
-    tools:
-      - "search_tool"
-      - "ContentTools.read_content"
-    tasks:
-      list_ideas:
-        description: "List of 5 interesting ideas to explore for an article about AI in healthcare."
-        expected_output: "Bullet point list of 5 ideas for an article."
-        async_execution: true
-      list_important_history:
-        description: "Research the history of AI in healthcare and identify the 5 most important events."
-        expected_output: "Bullet point list of 5 important events."
-        async_execution: true
-
-  writer:
-    role: "Writer"
-    backstory: |
-      With a flair for simplifying complex topics, you craft engaging narratives that captivate
-      and educate, bringing new discoveries to light in an accessible manner.
-    goal: "Narrate compelling tech stories around AI in healthcare"
-    verbose: true
-    tools:
-      - "search_tool"
-      - "ContentTools.read_content"
-    tasks:
-      write_article:
-        description: |
-          Compose an insightful article on AI in healthcare, including its history and the latest interesting ideas.
-        expected_output: "A 4-paragraph article about AI in healthcare."
-        context:
-          - "list_ideas"
-          - "list_important_history"
-        callback: "callback_function"
-
-dependencies:
-  - task: "write_article"
-    depends_on:
-      - "list_ideas"
-      - "list_important_history"
-
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/7_crew_ai_example_code.py

@@ -1,69 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """framework: "crewai"
-topic: "AI Advancements in 2024"
-roles:
-  researcher:
-    role: "Senior Research Analyst"
-    backstory: |
-      You are an expert at a technology research group, skilled in identifying trends and analyzing complex data.
-    goal: "Uncover cutting-edge developments in AI and data science"
-    verbose: true
-    allow_delegation: false
-    tools:
-      - "search_tool"
-    tasks:
-      task1:
-        description: |
-          Analyze 2024's AI advancements. Find major trends, new technologies, and their effects. Provide a detailed report.
-        expected_output: "A detailed report on major AI trends, new technologies, and their effects in 2024."
-  writer:
-    role: "Tech Content Strategist"
-    backstory: |
-      You are a content strategist known for making complex tech topics interesting and easy to understand.
-    goal: "Craft compelling content on tech advancements"
-    verbose: true
-    allow_delegation: true
-    tasks:
-      task2:
-        description: |
-          Create a blog post about major AI advancements using your insights. Make it interesting, clear, and suited for tech enthusiasts. It should be at least 4 paragraphs long.
-        expected_output: "An engaging blog post of at least 4 paragraphs about major AI advancements, suitable for tech enthusiasts."
-dependencies:
-  - task: "task2"
-    depends_on:
-      - "task1"
-"""
-
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/8_libre_translate.py

@@ -1,83 +0,0 @@
-# tools.py
-import requests
-from praisonai_tools import BaseTool
-
-class TranslatorTool(BaseTool):
-    name: str = "Translator Tool"
-    description: str = "Translate text from one language to another."
-
-    def _run(self, q: str, target: str):
-        url = "http://127.0.0.1:5000/translate"
-        payload = {
-            "q": q,
-            "source": "auto",  # Automatically detect the source language
-            "target": target
-        }
-        headers = {
-            "Content-Type": "application/json"
-        }
-
-        response = requests.post(url, json=payload, headers=headers)
-
-        if response.status_code == 200:
-            try:
-                data = response.json()
-                return data
-            except requests.exceptions.JSONDecodeError:
-                return "Failed to decode JSON from the response."
-        else:
-            return f"Request failed with status code: {response.status_code}"
-        
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "Writing an article about AI"
-roles:
-  article_writer:
-    role: "Article Writer"
-    backstory: "Experienced writer with a deep understanding of artificial intelligence and technology trends."
-    goal: "Write an informative and engaging article about AI."
-    tasks:
-      write_article:
-        description: "Write a comprehensive article about the latest developments and implications of AI."
-        expected_output: "A well-written article on AI in English."
-  french_translator:
-    role: "Translator"
-    backstory: "Fluent in French with expertise in translating technical and non-technical content accurately."
-    goal: "Translate the AI article into French."
-    tasks:
-      translate_to_french:
-        description: "Translate the English article on AI into French."
-        expected_output: "The AI article translated into French."
-    tools:
-      - "TranslatorTool"
-  german_translator:
-    role: "Translator"
-    backstory: "Proficient in German with a strong background in technical translation."
-    goal: "Translate the AI article into German."
-    tasks:
-      translate_to_german:
-        description: "Translate the English article on AI into German."
-        expected_output: "The AI article translated into German."
-    tools:
-      - "TranslatorTool"
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/9_mem0_praison_ai_integration.py

@@ -1,126 +0,0 @@
-# tools.py
-from mem0 import Memory
-from praisonai_tools import BaseTool
-
-class AddMemoryTool(BaseTool):
-    name: str = "Add Memory Tool"
-    description: str = ("This tool allows storing a new memory with user ID and optional metadata.\n"
-                        "Example:\n"
-                        "   - Input: text='I am working on improving my tennis skills. Suggest some online courses.', user_id='alice', metadata={'category': 'hobbies'}\n"
-                        "   - Output: Memory added with summary 'Improving her tennis skills. Looking for online suggestions.'")
-
-    def _run(self, text: str, user_id: str, metadata: dict = None):
-        m = Memory()
-        result = m.add(text, user_id=user_id, metadata=metadata)
-        return result
-
-class GetAllMemoriesTool(BaseTool):
-    name: str = "Get All Memories Tool"
-    description: str = ("This tool retrieves all stored memories.\n"
-                        "Example:\n"
-                        "   - Input: action='get_all'\n"
-                        "   - Output: List of all stored memories.")
-
-    def _run(self):
-        m = Memory()
-        result = m.get_all()
-        return result
-
-class SearchMemoryTool(BaseTool):
-    name: str = "Search Memory Tool"
-    description: str = ("This tool searches for specific memories based on a query and user ID.\n"
-                        "Example:\n"
-                        "   - Input: query='What are Alice's hobbies?', user_id='alice'\n"
-                        "   - Output: Search results related to Alice's hobbies.")
-
-    def _run(self, query: str, user_id: str):
-        m = Memory()
-        result = m.search(query=query, user_id=user_id)
-        return result
-
-class UpdateMemoryTool(BaseTool):
-    name: str = "Update Memory Tool"
-    description: str = ("This tool updates an existing memory by memory ID and new data.\n"
-                        "Example:\n"
-                        "   - Input: memory_id='cb032b42-0703-4b9c-954d-77c36abdd660', data='Likes to play tennis on weekends'\n"
-                        "   - Output: Memory updated to 'Likes to play tennis on weekends.'")
-
-    def _run(self, memory_id: str, data: str):
-        m = Memory()
-        result = m.update(memory_id=memory_id, data=data)
-        return result
-
-class MemoryHistoryTool(BaseTool):
-    name: str = "Memory History Tool"
-    description: str = ("This tool gets the history of changes made to a specific memory by memory ID.\n"
-                        "Example:\n"
-                        "   - Input: memory_id='cb032b42-0703-4b9c-954d-77c36abdd660'\n"
-                        "   - Output: History of the specified memory.")
-
-    def _run(self, memory_id: str):
-        m = Memory()
-        result = m.history(memory_id=memory_id)
-        return result
-
-
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-
-
-# Example agent_yaml content
-
-import os
-import yaml
-from praisonai import PraisonAI
-
-agent_yaml = """
-framework: "crewai"
-topic: "personal memory management"
-roles:
-  data_analyst:
-    role: "Data Analyst"
-    backstory: "Skilled in analyzing data to find trends and insights."
-    goal: "Analyze the data to provide useful insights and recommendations."
-    tasks:
-      research_ai_news:
-        description: "Analyze the latest AI news from July 2024 and trends to provide insights and recommendations."
-        expected_output: "Report with insights on the latest AI trends and personalized recommendations."
-    tools:
-      - "InternetSearchTool"
-  user_support:
-    role: "User Support"
-    backstory: "Expert in assisting users with queries and issues related to memory management."
-    goal: "Provide support to users for adding information to memories."
-    tasks:
-      support_users:
-        description: "Assist users in adding information to memories."
-        expected_output: "User queries resolved and memories managed effectively."
-    tools:
-      - "AddMemoryTool"
-      - "GetAllMemoriesTool"
-      - "SearchMemoryTool"
-
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below
-# os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') or "ENTER OPENAI_API_KEY HERE"
-openai_api_key = os.getenv("OPENAI_API_KEY")
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)

cookbooks/python/story.md

@@ -1,49 +0,0 @@
-```markdown
-# The Secret Meeting Spot
-
-![The Secret Meeting Spot](A serene and vibrant illustration of the Enchanted Forest, with Luna the Wise Owl perched on a branch of the ancient oak tree. Max the Brave Squirrel stands excitedly next to the tree, and Ella the Curious Fawn gazes at a mysterious map held by Luna. The forest is lush with magical flora and a soft, mystical glow, capturing the sense of wonder and the beginning of their adventure.)
-
-## Chapter 1: The Secret Meeting Spot
-
-Luna the Wise Owl had called for a secret meeting in the heart of the Enchanted Forest. Perched on the branch of the ancient oak tree, she awaited her friends with a mysterious map clutched in her talons. Max the Brave Squirrel scampered up to the tree, his eyes gleaming with excitement. Ella the Curious Fawn approached, her gaze fixed on the map that promised an adventure like no other. The forest around them was alive with magical flora, casting a soft, mystical glow that hinted at the wonders to come.
-
----
-
-# The Mysterious Riverbank
-
-![The Mysterious Riverbank](A lively scene at the bustling riverbank, featuring Benny the Playful Beaver busy constructing a sturdy wooden bridge. Luna, Max, Ella, and Benny are depicted crossing the bridge, while the river sparkles beneath them. Strange markings on the trees along the riverbank hint at the presence of others, creating an atmosphere of mystery and excitement.)
-
-## Chapter 2: The Mysterious Riverbank
-
-The group made their way to the bustling riverbank, where Benny the Playful Beaver was hard at work constructing a sturdy wooden bridge. Luna, Max, Ella, and Benny crossed the bridge together, the river sparkling beneath them. As they journeyed along the riverbank, they noticed strange markings on the trees, hinting at the presence of others. The air was filled with an atmosphere of mystery and excitement, urging them forward on their quest.
-
----
-
-# The Hidden Glade of Whispers
-
-![The Hidden Glade of Whispers](A magical hidden glade where the wind whispers secrets. Mia the Shy Rabbit stands with Luna, Max, Ella, and Benny, all gathered around as they decipher the whispers. Sunlight filters through the trees, and the glade is filled with ethereal light and soft colors, reflecting the growing bond and shared purpose of the friends as they solve the riddle together.)
-
-## Chapter 3: The Hidden Glade of Whispers
-
-In the heart of the forest lay the Hidden Glade of Whispers, a place where the wind itself shared secrets. Mia the Shy Rabbit joined Luna, Max, Ella, and Benny as they gathered to decipher the whispers. Sunlight filtered through the trees, casting an ethereal light that enveloped the glade in soft colors. It was here that the bond between the friends grew stronger, their shared purpose guiding them as they unraveled the riddle together.
-
----
-
-# The Cavern of Echoes
-
-![The Cavern of Echoes](Inside the dark and mysterious cavern, illuminated by Oliver the Inventive Raccoon's gadgets. The friends, including Luna, Max, Ella, Benny, and Mia, navigate the maze-like tunnels, facing challenges together. The echoes play tricks on their minds, portrayed with swirling shadows and light, highlighting their teamwork and trust as they discover a crucial clue.)
-
-## Chapter 4: The Cavern of Echoes
-
-The journey led them to the Cavern of Echoes, a dark and mysterious place where Oliver the Inventive Raccoon's gadgets illuminated their path. Together, Luna, Max, Ella, Benny, and Mia navigated the maze-like tunnels, each echo playing tricks on their minds. Swirling shadows and light portrayed the challenges they faced, their teamwork and trust shining through as they discovered a crucial clue that would lead them to their goal.
-
----
-
-# The Treasure of Friendship
-
-![The Treasure of Friendship](A serene meadow bathed in the warm glow of the setting sun, where the friends uncover a chest filled with seeds and a heartfelt message. The group, united and joyful, plants the seeds together, symbolizing growth and new adventures. The scene captures the essence of friendship and the realization of their true treasure, with a backdrop of nature's beauty and promise of more to come.)
-
-## Chapter 5: The Treasure of Friendship
-
-Finally, the friends emerged into a serene meadow bathed in the warm glow of the setting sun. There, they uncovered a chest filled with seeds and a heartfelt message. United and joyful, they planted the seeds together, a symbol of growth and new adventures on the horizon. The scene captured the essence of friendship, the realization of their true treasure, and the promise of nature's beauty and more adventures to come.
-```

cookbooks/tools/cli/app.py

@@ -0,0 +1,67 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import subprocess
+import os
+
+def run_terminal_command(command: str):
+    """
+    Run a terminal command and return its output.
+    """
+    try:
+        result = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+        print(f"Command output: {result}")
+        return {"stdout": result.stdout, "stderr": result.stderr}
+    except subprocess.CalledProcessError as e:
+        return {"error": str(e), "stdout": e.stdout, "stderr": e.stderr}
+
+def save_to_file(file_path: str, content: str):
+    """
+    Save the given content to the specified file path. Create the folder/file if it doesn't exist.
+    """
+    # Ensure the directory exists
+    os.makedirs(os.path.dirname(file_path), exist_ok=True)
+    
+    # Write the content to the file
+    with open(file_path, 'w') as file:
+        file.write(content)
+
+    return file_path
+
+# Create System Operations Agent
+system_ops_agent = Agent(
+    name="SystemOps",
+    role="System Operations Specialist",
+    goal="Execute and manage complex system operations and commands",
+    backstory="""You are an expert system administrator with deep knowledge of Unix/Linux systems.
+    You excel at running complex system commands, managing processes, and handling system operations.
+    You always validate commands before execution and ensure they are safe to run.""",
+    min_reflect=6,
+    max_reflect=10,
+    tools=[run_terminal_command, save_to_file],
+    llm="gpt-4o-mini"
+)
+
+# Create a complex task that tests various system operations
+task = Task(
+    name="system_analysis_task",
+    description="""Perform a comprehensive system analysis by executing the following operations in sequence:
+    1. Get system information (OS, kernel version)
+    2. List 5 running processes and sort them by CPU usage
+    3. Check disk space usage and list directories over 1GB
+    4. Display current system load and memory usage
+    5. List 5 network connections
+    6. Create a summary report with all findings in a text file called system_report.txt
+    
+    Do it step by step. One task at a time. 
+    Save only the Summary report in the file.
+    Use appropriate commands for each step and ensure proper error handling.""",
+    expected_output="A comprehensive system report containing all requested information saved in system_report.txt",
+    agent=system_ops_agent,
+)
+
+agents = PraisonAIAgents(
+    agents=[system_ops_agent],
+    tasks=[task],
+    process="sequential"
+)
+
+result = agents.start()

cookbooks/tools/e2b/agents.yaml

@@ -0,0 +1,46 @@
+framework: praisonai
+topic: crawl and extract all data from each url from this page https://news.ycombinator.com/
+roles:
+  web_scraper:
+    backstory: An expert in data extraction from websites, adept at navigating and
+      retrieving detailed information.
+    goal: Extract URLs from the source page
+    role: Web Scraper
+    output_file: urls.txt
+    tasks:
+      url_extraction:
+        description: Use the code_interpret run python code to extract all URLs from the page located
+          at https://news.ycombinator.com/.
+        expected_output: A list of URLs extracted from the source page.
+    tools:
+    - 'code_interpret'
+  url_data_extractor:
+    backstory: Specializes in crawling websites to gather comprehensive data, ensuring
+      nothing is missed from each link.
+    goal: Crawl each URL for data extraction
+    role: URL Data Extractor
+    context:
+      - web_scraper
+    tasks:
+      data_extraction:
+        description: Crawl each URL provided by the Web Scraper and extract all pertinent
+          data using the code_interpret to run python code.
+        expected_output: Raw data collected from each crawled URL.
+    tools:
+    - 'code_interpret'
+  data_organizer:
+    backstory: Known for keen attention to detail, capable of structuring vast amounts
+      of information into usable formats efficiently.
+    goal: Organize extracted data into a structured format
+    role: Data Organizer
+    context:
+      - url_data_extractor
+    tasks:
+      data_structuring:
+        description: Organize the raw data received from the URL Data Extractor into
+          a structured JSON format.
+        expected_output: A JSON file containing structured data from all extracted
+          URLs.
+    tools:
+    - 'code_interpret'
+dependencies: []

cookbooks/tools/e2b/app.py

@@ -0,0 +1,94 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents, error_logs
+import json
+from e2b_code_interpreter import Sandbox
+
+def code_interpret(code: str):
+    """
+    A function to demonstrate running Python code dynamically using e2b_code_interpreter.
+    """
+    print(f"\n{'='*50}\n> Running following AI-generated code:\n{code}\n{'='*50}")
+    exec_result = Sandbox().run_code(code)
+    if exec_result.error:
+        print("[Code Interpreter error]", exec_result.error)
+        return {"error": str(exec_result.error)}
+    else:
+        results = []
+        for result in exec_result.results:
+            if hasattr(result, '__iter__'):
+                results.extend(list(result))
+            else:
+                results.append(str(result))
+        logs = {"stdout": list(exec_result.logs.stdout), "stderr": list(exec_result.logs.stderr)}
+        return json.dumps({"results": results, "logs": logs})
+
+# 1) Create Agents
+web_scraper_agent = Agent(
+    name="WebScraper",
+    role="Web Scraper",
+    goal="Extract URLs from https://quotes.toscrape.com/",
+    backstory="An expert in data extraction from websites, adept at navigating and retrieving detailed information.",
+    tools=[code_interpret],
+    llm="gpt-4o"
+)
+
+url_data_extractor_agent = Agent(
+    name="URLDataExtractor",
+    role="URL Data Extractor",
+    goal="Crawl each URL for data extraction",
+    backstory="Specializes in crawling websites to gather comprehensive data, ensuring nothing is missed from each link.",
+    tools=[code_interpret],
+    llm="gpt-4o"
+)
+
+blog_writer_agent = Agent(
+    name="BlogWriter",
+    role="Creative Content Writer",
+    goal="Create engaging and insightful blog posts from provided data",
+    backstory="An experienced content creator and storyteller with a knack for weaving compelling narratives. Skilled at analyzing quotes and creating meaningful connections that resonate with readers.",
+    tools=[code_interpret],
+    llm="gpt-4o"
+)
+
+# 2) Create Tasks
+task_url_extraction = Task(
+    name="url_extraction_task",
+    description="""Use code_interpret to run Python code that fetches https://quotes.toscrape.com/
+    and extracts 5 URLs from the page, then outputs them as a list. Only first 5 urls""",
+    expected_output="A list of URLs extracted from the source page.",
+    agent=web_scraper_agent,
+    output_file="urls.txt" 
+)
+
+task_data_extraction = Task(
+    name="data_extraction_task",
+    description="""Take the URLs from url_extraction_task, crawl each URL and extract all pertinent
+    data using code_interpret to run Python code, then output raw txt, not the html.""",
+    expected_output="Raw data collected from each crawled URL.",
+    agent=url_data_extractor_agent,
+    context=[task_url_extraction]
+)
+
+blog_writing_task = Task(
+    name="blog_writing_task",
+    description="""Write an engaging blog post using the extracted quotes data. The blog post should:
+    1. Have a catchy title
+    2. Include relevant quotes from the data
+    3. Provide commentary and insights about the quotes
+    4. Be well-structured with introduction, body, and conclusion
+    5. Be approximately 500 words long""",
+    expected_output="A well-written blog post incorporating the extracted quotes with analysis",
+    agent=blog_writer_agent,
+    context=[task_data_extraction],
+    output_file="blog_post.txt"  
+)
+
+# 3) Create and run Agents manager
+agents_manager = PraisonAIAgents(
+    agents=[web_scraper_agent, url_data_extractor_agent, blog_writer_agent],
+    tasks=[task_url_extraction, task_data_extraction, blog_writing_task],
+    verbose=True,
+    process="hierarchical",
+    manager_llm="gpt-4o"
+)
+
+result = agents_manager.start()

cookbooks/tools/e2b/single_agent.py

@@ -0,0 +1,78 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents, error_logs
+import json, os
+from e2b_code_interpreter import Sandbox
+import subprocess
+
+def code_interpret(code: str):
+    """
+    A function to demonstrate running Python code dynamically using e2b_code_interpreter.
+    """
+    print(f"\n{'='*50}\n> Running following AI-generated code:\n{code}\n{'='*50}")
+    exec_result = Sandbox().run_code(code)
+    if exec_result.error:
+        print("[Code Interpreter error]", exec_result.error)
+        return {"error": str(exec_result.error)}
+    else:
+        results = []
+        for result in exec_result.results:
+            if hasattr(result, '__iter__'):
+                results.extend(list(result))
+            else:
+                results.append(str(result))
+        logs = {"stdout": list(exec_result.logs.stdout), "stderr": list(exec_result.logs.stderr)}
+        return json.dumps({"results": results, "logs": logs})
+
+def save_to_file(file_path: str, content: str):
+    """
+    Save the given content to the specified file path. Create the folder/file if it doesn't exist.
+    """
+    # Ensure the directory exists
+    os.makedirs(os.path.dirname(file_path), exist_ok=True)
+    
+    # Write the content to the file
+    with open(file_path, 'w') as file:
+        file.write(content)
+
+    return file_path
+
+def run_terminal_command(command: str):
+    """
+    Run a terminal command and return its output.
+    """
+    try:
+        result = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+        print(f"Command output: {result}")
+        return {"stdout": result.stdout, "stderr": result.stderr}
+    except subprocess.CalledProcessError as e:
+        return {"error": str(e), "stdout": e.stdout, "stderr": e.stderr}
+
+
+# 1) Create Agents
+web_scraper_agent = Agent(
+    name="WebScraper",
+    role="Web Scraper",
+    goal="Extract URLs from https://news.ycombinator.com/",
+    backstory="An expert in data extraction from websites, adept at navigating and retrieving detailed information and saving it to a file. and just check if the file is created using run_terminal_command tool",
+    min_reflect=3,
+    max_reflect=5,
+    tools=[code_interpret, save_to_file, run_terminal_command],
+    llm="gpt-4o"
+)
+
+task = Task(
+    name="url_extraction_task",
+    description="""Use code_interpret to run Python code that fetches https://news.ycombinator.com/
+    and extracts first 20 URLs from the page, with full path, then outputs them in a txt file.""",
+    expected_output="A list of URLs extracted from the source page in a txt file.",
+    agent=web_scraper_agent,
+)
+
+agents = PraisonAIAgents(
+    agents=[web_scraper_agent],
+    tasks=[task],
+    process="sequential"
+)
+
+result = agents.start()
+
+print(result)   

cookbooks/tools/e2b/tools.py

@@ -0,0 +1,24 @@
+import json
+from e2b_code_interpreter import Sandbox
+
+def code_interpret(code: str):
+    print(f"\n{'='*50}\n> Running following AI-generated code:\n{code}\n{'='*50}")
+    exec = Sandbox().run_code(code)
+
+    if exec.error:
+        print("[Code Interpreter error]", exec.error)
+        return {"error": str(exec.error)}
+    else:
+        results = []
+        for result in exec.results:
+            # Ensure each result is a list or a simple Python type
+            if hasattr(result, '__iter__'):
+                result_list = list(result)  # Convert Result to list
+                results.extend(result_list)
+            else:
+                results.append(str(result))  # Fall back to string
+
+        logs = {"stdout": list(exec.logs.stdout), "stderr": list(exec.logs.stderr)}
+
+        # Now everything is JSON-serialisable
+        return json.dumps({"results": results, "logs": logs})

cookbooks/tools/tools.py

@@ -1,72 +0,0 @@
-# tools.py
-from duckduckgo_search import DDGS
-from praisonai_tools import BaseTool
-
-class InternetSearchTool(BaseTool):
-    name: str = "InternetSearchTool"
-    description: str = "Search Internet for relevant information based on a query or latest news"
-
-    def _run(self, query: str):
-        ddgs = DDGS()
-        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
-        return results
-    
-from langchain_community.tools.pubmed.tool import PubmedQueryRun
-from langchain_community.tools.reddit_search.tool import RedditSearchRun
-# pip install tavily-python
-from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
-from langchain_community.utilities.you import YouSearchAPIWrapper
-# pip install youtube_search
-from langchain_community.tools import YouTubeSearchTool
-# pip install wikipedia
-from langchain_community.utilities import WikipediaAPIWrapper
-
-class TavilyTool(BaseTool):
-    name: str = "TavilyTool"
-    description: str = "Search Tavily for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = TavilySearchAPIWrapper()
-        results = api_wrapper.results(query=query, max_results=5)
-        return results
-
-class YouSearchTool(BaseTool):
-    name: str = "You Search Tool"
-    description: str = "Search You.com for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = YouSearchAPIWrapper()
-        results = api_wrapper.results(query=query, max_results=5)
-        return results
-
-class WikipediaSearchTool(BaseTool):
-    name: str = "WikipediaSearchTool"
-    description: str = "Search Wikipedia for relevant information based on a query."
-
-    def _run(self, query: str):
-        api_wrapper = WikipediaAPIWrapper(top_k_results=4, doc_content_chars_max=100)
-        results = api_wrapper.load(query=query)
-        return results
-
-# pip install open-interpreter
-from interpreter import interpreter
-from praisonai_tools import BaseTool
-
-class OpenInterpreterTool(BaseTool):
-    name: str = "Open Interpreter Tool"
-    description: str = "Use Open Interpreter configured to create and execute code based on natural language commands."
-
-    def __init__(self):
-        super().__init__()
-        interpreter.auto_run = True
-
-    def _run(self, code: str):
-        result = interpreter.chat(code)
-        return result
-
-# Example usage
-if __name__ == "__main__":
-    tool = OpenInterpreterTool()
-    code = "Plot AAPL and META's normalized stock prices"
-    output = tool._run(code)
-    print(output)

docker-compose.yml

@@ -0,0 +1,21 @@
+services:
+  ui:
+    build:
+      context: .
+      dockerfile: Dockerfile.ui
+    volumes:
+      - .:/app
+    ports:
+      - "8082:8082"
+    environment:
+      FLASK_ENV: development
+      CHAINLIT_PORT: "8082"
+      CHAINLIT_HOST: "0.0.0.0"
+      OPENAI_API_KEY: ${OPENAI_API_KEY}
+      CHAINLIT_AUTH_SECRET: ${CHAINLIT_AUTH_SECRET}
+    command: praisonai ui --host 0.0.0.0
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8082"]
+      interval: 10s
+      timeout: 5s
+      retries: 3

docs/api-reference/index.mdx

@@ -1,6 +1,7 @@
 ---
 title: 'API Reference'
 description: 'PraisonAI API documentation with framework-specific details'
+icon: "book-open"
 ---
 
 # API Reference

docs/api.mdx

@@ -1,4 +1,8 @@
-# API Reference
+---
+title: "API Reference"
+description: "Complete API reference for PraisonAI, including core modules, installation options, and framework-specific features"
+icon: "code"
+---
 
 ## Core Modules